US dataswitch to UK
Mathematicians
maintaining knowledge in the field by reading professional journals, assembling sets of assumptions and performing computations and applying methods of numerical analysis. If that's your week, this page is about your job.
The honest answer
This job is splitting in two: disseminating research by writing reports is work AI now does quickly and cheaply, and mentoring others on mathematical techniques is work it can't touch.
Which half fills your week decides your exposure. That is more in your control than it sounds.
Your week, as this page understands it
Conduct research in fundamental mathematics or in application of mathematical techniques to science, management, and other fields. Solve problems in various fields using mathematical methods. The job title says “mathematicians”. The real job is the part underneath: mentoring others on mathematical techniques. That is the thing someone has to be right about.
The exposed part of this job is specific, and we won’t pretend it is coming back. But mathematicians is not one task. It is 12 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is mentoring others on mathematical techniques, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 48%
- changing shape
- 42%
- staying human
- 10%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 59 out of 100 (52–65 allowing for uncertainty): partial exposure, across 12 scored tasks. The number is the support for the sentence above it, not a headline about anyone’s future.
How we know this
What is measured: Every published task statement for mathematicians is rated on five dimensions: can a model produce the output, does the work need a body in a room, does it need a legally accountable person, does it depend on a person being trusted in the moment, and how much data exists. A published formula turns those five ratings into the score; the model never writes the number.
How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.
Release: 2026-q4.1, scores computed 2026-08-05. Read the full method.
Your job, task by task
These are the official task statements for this occupation, in plain English, sorted by what the evidence says is happening to each one. The official wording sits under every line so you can check the rewrite against it.
- One row is marked low confidence, so treat it as a ballpark rather than a fine measurement.
- 2 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
- Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.
Shifting to AI
6 tasksTasks today’s tools can already do most of. This is the part we will not soften: where these rows are the bulk of your week, the week changes.
Disseminating research by writing reports
This is reading one thing and writing another: research in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Disseminate research by writing reports, publishing papers, or presenting at professional conferences.” (O*NET task statement)
How this row was scored
Exposure score: 70 out of 100 (63–77 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Writing up results in standard form is well-trodden ground for AI, with the author checking.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 4/4.
Addressing the relationships of quantities
It is the same call made over and over on the relationships of quantities, with a right answer to check it against. That is what a model is trained on.
importance 4 · CoreSource: “Address the relationships of quantities, magnitudes, and forms through the use of numbers and symbols.” (O*NET task statement)
How this row was scored
Exposure score: 65 out of 100 (53–77 allowing for uncertainty): high exposure, low confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Symbolic work is thoroughly documented, though deciding which relationships matter is still the mathematician's call.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Performing computations and applying methods of numerical analysis to data
This is reading one thing and writing another: computations in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Perform computations and apply methods of numerical analysis to data.” (O*NET task statement)
How this row was scored
Exposure score: 83 out of 100 (79–87 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Numerical computation is exactly what software and AI do fastest and most reliably.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Applying mathematical theories and techniques to the solution of practical problems in business
This is reading one thing and writing another: mathematical theories in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Apply mathematical theories and techniques to the solution of practical problems in business, engineering, the sciences, or other fields.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (68–82 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Applying known techniques to practical problems is documented work AI handles with checking.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Developing mathematical or statistical models of phenomena to be used for analysis or for computational simulation
This is reading one thing and writing another: mathematical in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Develop mathematical or statistical models of phenomena to be used for analysis or for computational simulation.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (68–82 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Model building follows documented methods, and AI produces usable models for the mathematician to check.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Changing shape
5 tasksTasks where the machine takes the producing and a person keeps the checking, the deciding, or the answering-for-it. For most jobs this is the biggest group, and it is where "transformation, not termination" is literally visible.
Maintaining knowledge in the field by reading professional journals
The software now makes the first pass at knowledge, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Maintain knowledge in the field by reading professional journals, talking with other mathematicians, and attending professional conferences.” (O*NET task statement)
How this row was scored
Exposure score: 48 out of 100 (41–55 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Reading and summarizing the literature is something AI does well, though conferences involve people.
The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Assembling sets of assumptions and explore the consequences of each set
The software now makes the first pass at sets of assumptions, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Assemble sets of assumptions, and explore the consequences of each set.” (O*NET task statement)
How this row was scored
Exposure score: 58 out of 100 (51–65 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Machines can explore what assumptions imply, but choosing worthwhile assumptions stays with the mathematician.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Developing new principles and new relationships between existing mathematical principles to advance mathematical science
The software now makes the first pass at new principles, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Develop new principles and new relationships between existing mathematical principles to advance mathematical science.” (O*NET task statement)
How this row was scored
Exposure score: 40 out of 100 (36–44 allowing for uncertainty): partial exposure, high confidence.
Why it sits in this group: the same decision, made over and over.
The rating behind it: Genuinely new mathematics is where machines produce fragments rather than finished results.
The five ratings: output a model can produce 1/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Conducting research to extend mathematical knowledge in traditional areas
The software now makes the first pass at research, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Conduct research to extend mathematical knowledge in traditional areas, such as algebra, geometry, probability, and logic.” (O*NET task statement)
How this row was scored
Exposure score: 48 out of 100 (44–52 allowing for uncertainty): partial exposure, high confidence.
Why it sits in this group: the same decision, made over and over.
The rating behind it: Extending mathematical knowledge means producing results nobody has yet, where AI offers only openings.
The five ratings: output a model can produce 1/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Staying human
1 taskTasks that stay with a person, because they happen in the physical world, because the rules need someone accountable, or because the value is that a specific person does them.
Mentoring others on mathematical techniques
The value here is that a specific person handles this work and stands behind it. That is earned, not computed.
importance 4 · CoreSource: “Mentor others on mathematical techniques.” (O*NET task statement)
How this row was scored
Exposure score: 32 out of 100 (25–39 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Explanations generate easily, but mentoring works through a trusted relationship with a particular person.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 3/4 · how much data exists 3/4.
Show the other 2 tasks
Developing computational methods for solving problems that occur in areas of science and engineering or that come from applications in business or industry
shifting to AIIt is the same call made over and over on computational methods, with a right answer to check it against. That is what a model is trained on.
importance 3 · CoreSource: “Develop computational methods for solving problems that occur in areas of science and engineering or that come from applications in business or industry.” (O*NET task statement)
How this row was scored
Exposure score: 65 out of 100 (58–72 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Inventing new computational methods needs original insight, even though the background material is fully documented.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Designing, analyzing and decipher encryption systems designed to transmit military, political, financial or law-enforcement-related information in code
changing shapeThe software now makes the first pass at decipher encryption systems, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 3 · SupplementalSource: “Design, analyze, and decipher encryption systems designed to transmit military, political, financial, or law-enforcement-related information in code.” (O*NET task statement)
How this row was scored
Exposure score: 58 out of 100 (51–65 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Cryptographic methods are well documented, but a sound new system needs hard expert scrutiny.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
What this job pays, and how many people do it
- Median pay
- $126,710a year, the middle of the range, so half earn more and half earn less.bls-oews, 2025 · May 2025 estimates (national_M2025_dl.xlsx)
How we know this
Source: bls-oews
Reference period: May 2025 estimates (national_M2025_dl.xlsx)
Rounding: Shown as published.
- People doing this job
- 2,030in the US, 2025.bls-oews · May 2025 estimates (national_M2025_dl.xlsx)
What is deliberately not here: a forecast of how many of these jobs exist in ten years. Where an official projection exists for a market we publish it with its vintage; where it does not, we leave the space empty rather than borrow the other country’s number.
Why this is shifting
The reason is boringly specific. Most of what is shifting here is reading one thing and writing another: the relationships of quantities in, a record out. The rows above are exactly that shape: disseminating research by writing reports and addressing the relationships of quantities. What it cannot do is be trusted in person, which is what others on mathematical techniques run on: someone specific doing it and standing behind it. Which is why this page talks about your tasks changing, not your job ending.
Your move
Over a pint: what I’d tell you if you were my friend
The exposed part of your job is the biggest part, and I am not going to dress that up: disseminating research by writing reports is work today's tools do quickly and cheaply, and that is not coming back.
So, given all that: 48% of this job's task weight sits in rows the software is already learning, 42% in rows that change shape rather than disappear, and 10% in rows it is nowhere near. That is the position, measured across 12 scored tasks. It is not a forecast about you.
What you have that the software does not is mentoring others on mathematical techniques, plus the years of knowing when something looks wrong before you can say why. That is the raw material for everything below.
This week: one thing
Sit on the machine's side of the desk. Pick one real piece of the relationships of quantities you would normally do yourself, let whatever software you already have take the first pass at it, and then go through what it produced and write down every single thing it got wrong. One evening this week. Do not fix anything yet. Just catch it.
- What you end up holding
- a written list of the machine’s mistakes, in your handwriting
- How long it takes
- an evening, or an hour if you pick one job rather than one client
If you can’t run software on your employer’s or your clients’ data: Do the same hunt on paper. Take one printed piece of the relationships of quantities, work through it the way you always do, and mark every point where you made a call rather than followed a rule. Same evening, same list, nothing to log into and nobody to ask permission from. That list is the same artifact: it is the judgment written down.
Over the next 90 days
Change one sentence about what you do. Not on a CV. Out loud, to whoever pays you. From “my job is disseminating research by writing reports” to “I check what the software does and tell you what it means.” Your error list from this week is what makes that sentence true instead of a claim, so use it: show it once, to one person, inside the next ninety days. Same skills, priced as judgment rather than as typing.
Over the next 12 months
Walk toward the end of this job that answers for things, and get it recognised. Pick the one part of mentoring others on mathematical techniques you are already best at, and spend the year making it formal: a qualification, a named responsibility, a specialism people ask for by name. Price it honestly: that is evenings, it is months rather than weeks, and the seats are competitive because everyone in your position is looking at the same door. Before you pay for anything, use CareerOneStop - Find local training. It is free, it is the Labor Department's own service, and it is listed below with the rest of the free routes.
The roads out of here, and why I am not sending you down them
I looked at the obvious moves out of this job, and here is what I found.
I checked the 12 nearest US occupations to mathematicians (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was physicists: only about 6% of its durable work is work you already do. Your own job splits about 48/52: that share of the list sits in the top exposure band and the rest does not. On this evidence the honest move is inside the job rather than out of it. Become the person who owns “maintain knowledge in the field by reading professional journals, talking with other…”, and let the exposed end go.
How that was checked: this job was compared against all 830 US occupations in this release on their official task statements, and the 12 nearest were examined one by one. A move that turns on an industry, an employer or a qualification rather than on the work itself will not show up in a check like that. And this release carries no licence register, so anything you are weighing needs that looked up separately.
3 moves I checked and rejected
These are the obvious-looking jumps. They are here with their reasons rather than quietly dropped, because the ones that fail are worth knowing about. It is one less thing to turn over at night.
Physicists
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already disseminate research by writing reports, publishing papers, or presenting at professional conferences, and their equivalent is to report experimental results by writing papers for scientific journals or by presenting information…. Across both published task lists that is about 6% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 6% of the durable side of that job. That is a different job, not a next step.
Astronomers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already mentor others on mathematical techniques, and their equivalent is to mentor graduate students and junior colleagues. Across both published task lists that is about 6% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 6% of the durable side of that job. That is a different job, not a next step.
Business Teachers, Postsecondary
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already mentor others on mathematical techniques, and their equivalent is to mentor new faculty. Across both published task lists that is about 4% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 4% of the durable side of that job. That is a different job, not a next step. It is a pay cut, in those words: $99,080 against your $126,710, 21.8% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
What I’d stop worrying about
A friend tells you what not to spend fear on. This is that list.
The headline number you read somewhere
The big “X% of jobs” figures are about the whole economy, not about you. The number that describes your job is on this page: 48% of its task weight, across 12 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
“It’s too late for me to become something else”
You are not starting from zero, and the page shows why: mentoring others on mathematical techniques is work the software cannot do and you already do it. The move above is a repricing of what you know, not a new career. Nobody who has just left college has that.
“I should learn to code”
Almost certainly not. The value in your job is moving toward checking, deciding and answering for the output, not toward writing the software. A weekend of Python will not change your position; the error list from this week will.
The “obvious” next job everyone suggests
I checked the obvious moves and most of them did not survive. The reasons are printed with the routes above, including the pay and the gate. A move that fails on the numbers is worth knowing about so you can stop turning it over at night.
You are reading the United States figures
The United Kingdom splits this work across more than one official group, of which Actuaries, economists and statisticians is the closest. The pay and employment figures are not directly comparable, and we do not average them together.
Switch to the United Kingdom page →partial match
In UK official statistics this job is counted as Actuaries, economists and statisticians. Pay and employment stay on this page’s own group; the task list and the scores do not cross over.
Your route through this
Where to go next, and what it costs
Free, and complete
The moves above cost nothing. These are the real services that go with them: public, government-funded, and free at the point of use. Nothing on this page is behind an email address or a payment.
Anywhere in the US:
CareerOneStop - Find local training
Search what's running near you, from the Labor Department's own database, before anyone sells you a course.
Free to search; individual programs vary, and some are funded
Anywhere in the US:
An American Job Center will sit down with you for free. Find yours by ZIP code.
Free
Anywhere in the US:
CareerOneStop - Licensed occupations finder
Check what your state actually requires before you pay for anything.
Free
Anywhere in the US:
Free
No Space for this job, but one for what is happening to it
Nothing Collab365 runs is built for mathematicians, and we are not going to point you at the nearest one and call it a fit.
There is one that is not about a job title at all. The AI Authority is about being the person who directs these tools at work rather than the person they get compared to. That is worth saying here, because 48% of the work on this page is already inside what they can do.
7 days free, no card needed. Explore up to 2 Spaces before you choose a plan: you pick a plan later, not now.
The AI Authority is a general community about working with AI, not a course for mathematicians. You do not need it to act on anything here: the moves above cost nothing and stand on their own. The data on this page is the same either way.
Noted, and thank you. We’ll email you if a Space for mathematicians launches. Nothing else.
That did not look like an email address, so nothing was saved. Have another go below.
We could not save that. The fault is ours, not yours, and your address was not stored. Please try again later.
No deadline on any of this. The page will still be here, and the data is refreshed on a published schedule rather than when someone wants a headline.
Questions people ask about this job
- Will AI replace Mathematicians?
- Not as a job, but it is already doing parts of the work. Across the 12 official task statements scored for Mathematicians (United States, SOC 15-2021), 48% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 59 out of 100 (range 52–65, band: partial). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.
- Which tasks in “Mathematicians” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Perform computations and apply methods of numerical analysis to data” (83/100, very high); “Apply mathematical theories and techniques to the solution of practical problems in business, engineering, the sciences, or other fields” (75/100, high); “Develop mathematical or statistical models of phenomena to be used for analysis or for computational simulation” (75/100, high). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
- Which tasks in “Mathematicians” stay human?
- About 10% of this job's task weight sits in work that scores low for AI exposure. The lowest-scoring tasks in release 2026-q4.1 are: “Mentor others on mathematical techniques” (32/100, low); “Develop new principles and new relationships between existing mathematical principles to advance mathematical science” (40/100, partial); “Conduct research to extend mathematical knowledge in traditional areas, such as algebra, geometry, probability, and logic” (48/100, partial). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.
- What should someone working in “Mathematicians” do about AI?
- Start from the ledger rather than the headline: 48% of this job's weighted core work is exposed, and roughly 10% is not. The practical move is to spend more of your week on the tasks that score low, the ones above, and to get fluent at directing AI through the tasks that score high, because those are the parts that change whether or not you are ready for them. This page does not predict your job, and nothing here is career advice tailored to you: the score describes the occupation, not the person.
- How is the AI exposure score for Mathematicians calculated?
- Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 12 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.
Where these numbers come from
About the data on this page
- One row is marked low confidence, so treat it as a ballpark rather than a fine measurement.
- The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
- 2 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
- Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.
- Task statements
- onet-dbProcessing: catalogue-bridge → onet-im-rt-weighting → task-scoring → score-aggregation
- Task weights
- onet-db (im-rt)
- Scores
- Rubric task_scoring_v1.0, model claude-opus-5, computed 2026-08-05.
- Pay and employment
- bls-oews (May 2025 estimates (national_M2025_dl.xlsx))bls-oews (May 2025 estimates (national_M2025_dl.xlsx))
Figures on this page come from release 2026-q4.1, published 2026-08-05. Every release keeps its own permanent address, so a figure you cite in March is still there, unchanged, in November.
The plain-English wording on this page is assembled directly from the task statements and the published ratings, not written by hand for this occupation. That is why it is specific, and it is also why we say so.
The routes and free resources further up are today’s, not the release’s (last reviewed 2026-08-05). A route is an offer, not a historical fact, so it moves on its own clock.
Using these figures?
Cite this
Everything on this site is published under CC BY 4.0. Quote it, chart it, sell something built on it. Just say where it came from, and cite the dated release rather than the site, so the figure you quote stays checkable.
Plain text
Collab365 (2026). Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1 (methodVersion 2.0.0, promptVersion task_scoring_v1.0). https://futureproof.collab365.com/data/2026-q4.1. Licensed CC BY 4.0. Built with O*NET data (USDOL/ETA, CC BY 4.0); ONS data (Open Government Licence v3.0); GAISI task framework (arXiv:2507.22748, MIT); BLS data (public domain).
BibTeX
@misc{collab365futureproof2026q41,
title = {Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1},
author = {{Collab365}},
year = {2026},
url = {https://futureproof.collab365.com/data/2026-q4.1},
note = {Release 2026-q4.1, methodVersion 2.0.0, promptVersion task_scoring_v1.0, CC BY 4.0}
}Data as of release 2026-q4.1, published . Releases never change after publication; when the figures move, a new dated release is published beside this one and this one stays exactly where it is.