Often handed to AI
Task areas most often handled directively in observed AI conversations — candidates to delegate with light review.
- Compute data such as gear dimensions and machine settings, applying knowledge of shop mathematics. · 1.2%
Multiple Machine Tool Setters, Operators, and Tenders, Metal and Plastic
Occupation · SOC 51-4081.00
Set up, operate, or tend more than one type of cutting or forming machine tool or robot.
Also called: CNC Machine Setter (Computer Numerically Controlled Machine Setter) · Cell Technician · Machine Operator · Machine Technician · Fabrication Set-Up Person · Injection Molding Technician · Mold Setter · Production Operator · Shear Operator · Tooling Set-Up Person · Automatic Wheel-Line Operator · Ballistics Laboratory Gunsmith
Job family: Production Occupations
Take this to your AI
A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch
/roles/role-51-4081-00/context.md directly.
AI work map
A fast read on where AI already shows up in this occupation, where it stays a copilot, where humans remain in the loop, and what the labor market is doing. Built from observed Claude.ai conversations mapped to O*NET tasks and from published research — measures of usage and exposure, not advice or predictions that the job is going away.
Keep a human in the loop
Task areas where a human was still judged necessary in a large share of observed conversations — not a safety ruling, an observed-need signal.
- Compute data such as gear dimensions and machine settings, applying knowledge of shop mathematics. · 90.7% need a human
Skills it runs on
The capabilities O*NET rates most important for this occupation — the human ground the work is built on.
See all skills →Market signal
Independent published positions, read together — not a forecast.
- Declining employment outlook (-0.5% by 2034)
- 12,800 openings/yr
- Moderate AI exposure
- Observed AI use leans automation
- Median pay $46,060/yr
- Work is mostly on-site (not teleworkable)
- 2013 flagged this for computerization (81st); today's AI task-overlap is comparatively low (28th)
28th-percentile task overlap — yet about 12,800 openings a year (-0.5% projected, BLS), and observed AI use leans 1864% copilot, not hand-off (AEI) . What exposure means →
AI & job outlook
What today's research says about this occupation's exposure to AI, how AI is actually being used in it, and where employment is headed. These are positions within published studies — measures of exposure and usage, not predictions that this job will disappear.
Exposure to current AI
Each study uses its own scale, so the raw scores are not comparable across rows — the percentile (this job's rank among all U.S. occupations with data) is the comparable figure, and sizes the bars.
| Measure | Rank vs all occupations | Percentile | Score |
|---|---|---|---|
| Overall AI exposure (Felten et al.) Low | 27th | -0.7 | |
| LLM task exposure, γ (OpenAI / Eloundou) Low | 17th | 0.1 | |
| AI assistant applicability (Microsoft) Moderate | 44th | 0.1 |
OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.1), with simple added tooling (β 0.1), and including AI-powered software (γ 0.1). Higher means more of the job's tasks could be done at least twice as fast — not that they will be automated away.
This job mostly cannot be done remotely (Dingel–Neiman) — its hands-on tasks sit outside what software-based AI reaches.
Mixed signals. Today's AI/LLM studies show relatively low exposure for this job, but the older (2013) Frey–Osborne work rated it higher for computerization and robotics. Different eras, different technologies — the AI measures above reflect the current state.
Historical automation estimate (2013)
A pre-LLM (2013) estimate of how automatable this job is by computerization and robotics. Shown for historical context only — it is not part of any current AI ranking.
Frey–Osborne probability 0.9 · 81st percentile among occupations · High
Job outlook
Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.
| Outlook | Declining · -0.5% by 2034 |
| Projected annual openings | 12,800 |
| Employment 2024 → 2034 | 131,000 → 130,300 |
“Annual openings” counts new jobs plus replacements for workers who leave the occupation, so it can be large even when growth is modest.
Where this work sits on the global GenAI gradient
The ILO's 2025 global study scores generative-AI exposure on the international ISCO-08 occupation system, not US SOC. Bridged through the published (and approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 crosswalk, this US occupation corresponds to the international occupation below. Exposure here means how much of the work's tasks today's AI can attempt — task overlap, not automation, adoption, or jobs lost.
Multiple Machine Tool Setters, Operators, and Tenders, Metal and Plastic sits at the 28th percentile of 427
occupations on the global GenAI task-exposure gradient
— exposure rose from 2023 to 2025. Each dot is one occupation; the
ringed one is this work. Exposure is task overlap, not automation or jobs lost.
18% mean task exposure (2025)
28th percentile of 427 placed occupations
+2 pts shift 2023 → 2025
| International occupation (ISCO-08) | Task exposure (2025) | Most tasks fall in |
|---|---|---|
| Metal Working Machine Tool Setters and Operators · 7223 | 18% | Not exposed |
Read the whole six-band gradient on the GenAI exposure gradient page. The crosswalk is approximate: a US occupation can map to several international ones, and the ILO scores describe the international occupation, not this exact US role.
Working with AI in this job
How people actually apply AI to this occupation's tasks, from Claude.ai (Free and Pro) conversations in the Anthropic Economic Index, 2026-01-15. This is one AI assistant's consumer sample — not all AI, not the whole workforce. Autonomy and the collaboration mix are model-rated estimates; figures below the sample floor are hidden.
| Augmentation vs. automation | 18.6% working with AI · 58.5% handed to AI |
| Most common way people use AI here | Directive · AI does it; you give the instruction |
| Typical AI autonomy | 3.0 / 5 · higher = AI acts more independently |
What people delegate to AI
The role's most common tasks in AI conversations, each tagged with how people work with the AI on it. “Usage” is the share of observed conversations, not of the job.
| Task | How | Usage |
|---|---|---|
| Compute data such as gear dimensions and machine settings, applying knowledge of shop mathematics. | Directive | 1.2% |
Where a human is still needed
Tasks where the model most often judged that a person remained necessary — a useful read on the current boundary, not a guarantee.
| Compute data such as gear dimensions and machine settings, applying knowledge of shop mathematics. | 90.7% |
What people most often hand AI here
Example prompts phrased from the tasks people most often delegate to AI in this occupation (Anthropic Economic Index). Each shows the underlying measured task and its share of observed AI use. They are suggested phrasings of real tasks — starting points, not endorsed instructions.
-
Help me compute data such as gear dimensions and machine settings, applying knowledge of shop mathematics.From: Compute data such as gear dimensions and machine settings, applying knowledge of shop mathematics. · 1.2% of measured AI use · directive
Tasks
All 21 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.
- Inspect workpieces for defects, and measure workpieces to determine accuracy of machine operation, using rules, templates, or other measuring instruments. Core · importance 4.6
- Position, adjust, and secure stock material or workpieces against stops, on arbors, or in chucks, fixtures, or automatic feeding mechanisms, manually or using hoists. Core · importance 4.4
- Read blueprints or job orders to determine product specifications and tooling instructions and to plan operational sequences. Core · importance 4.3
- Select, install, and adjust alignment of drills, cutters, dies, guides, and holding devices, using templates, measuring instruments, and hand tools. Core · importance 4.3
- Observe machine operation to detect workpiece defects or machine malfunctions, adjusting machines as necessary. Core · importance 4.2
- Set up and operate machines, such as lathes, cutters, shears, borers, millers, grinders, presses, drills, or auxiliary machines, to make metallic and plastic workpieces. Core · importance 4.2
- Measure and mark reference points and cutting lines on workpieces, using traced templates, compasses, and rules. Supplemental · importance 4.1
- Change worn machine accessories, such as cutting tools or brushes, using hand tools. Core · importance 4.1
- Set machine stops or guides to specified lengths as indicated by scales, rules, or templates. Core · importance 4.0
- Select the proper coolants and lubricants and start their flow. Core · importance 4.0
- Perform minor machine maintenance, such as oiling or cleaning machines, dies, or workpieces, or adding coolant to machine reservoirs. Core · importance 4.0
- Remove burrs, sharp edges, rust, or scale from workpieces, using files, hand grinders, wire brushes, or power tools. Core · importance 4.0
- Make minor electrical and mechanical repairs and adjustments to machines and notify supervisors when major service is required. Core · importance 3.9
- Compute data, such as gear dimensions or machine settings, applying knowledge of shop mathematics. Core · importance 3.9
- Start machines and turn handwheels or valves to engage feeding, cooling, and lubricating mechanisms. Core · importance 3.9
- Move controls or mount gears, cams, or templates in machines to set feed rates and cutting speeds, depths, and angles. Core · importance 3.8
- Instruct other workers in machine set-up and operation. Core · importance 3.7
- Write programs for computer numerical control (CNC) machines to cut metal and plastic materials. Supplemental · importance 3.7
- Record operational data, such as pressure readings, lengths of strokes, feed rates, or speeds. Core · importance 3.6
- Align layout marks with dies or blades. Supplemental · importance 3.5
- Extract or lift jammed pieces from machines, using fingers, wire hooks, or lift bars. Core · importance 3.5
Work activities
- Measure dimensions of completed products or workpieces to verify conformance to specifications.
- Watch operating equipment to detect malfunctions.
- Read work orders or other instructions to determine product specifications or materials requirements.
- Review blueprints or other instructions to determine operational methods or sequences.
- Operate grinding equipment.
- Operate cutting equipment.
- Mount materials or workpieces onto production equipment.
- Select production equipment according to product specifications.
- Mount attachments or tools onto production equipment.
- Set equipment controls to meet cutting specifications.
- Replace worn equipment components.
- Repair production equipment or tools.
- Notify others of equipment repair or maintenance needs.
- Adjust equipment controls to regulate flow of production materials or products.
- Adjust equipment controls to regulate coolant flow.
- Adjust equipment controls to regulate flow of water, cleaning solutions, or other liquids.
- Clean production equipment.
- Maintain production or processing equipment.
- Lubricate production equipment.
- Select production input materials.
- Operate metal or plastic forming equipment.
- Smooth metal surfaces or edges.
- Instruct workers to use equipment or perform technical procedures.
- Record operational or production data.
- Clear equipment jams.
- Set equipment guides, stops, spacers, or other fixtures.
- Calculate dimensions of workpieces, products, or equipment.
- Align parts or workpieces to ensure proper assembly.
- Measure materials to mark reference points, cutting lines, or other indicators.
- Program equipment to perform production tasks.
- Write computer programming code.
Knowledge, skills & abilities
O*NET importance rating, from 1 (not important) to 5 (extremely important).
Knowledge
| Production and Processing | 4.1 | |
| Mechanical | 3.9 | |
| Mathematics | 3.6 | |
| English Language | 3.3 | |
| Design | 3.1 |
Transferable skills
Abilities
| Arm-Hand Steadiness | 3.9 | |
| Control Precision | 3.9 | |
| Near Vision | 3.9 | |
| Manual Dexterity | 3.8 | |
| Problem Sensitivity | 3.6 | |
| Visualization | 3.4 | |
| Information Ordering | 3.3 | |
| Oral Comprehension | 3.1 | |
| Written Comprehension | 3.1 | |
| Selective Attention | 3.1 | |
| Finger Dexterity | 3.1 | |
| Multilimb Coordination | 3.1 | |
| Reaction Time | 3.1 | |
| Far Vision | 3.1 | |
| Speech Recognition | 3.1 | |
| Deductive Reasoning | 3.0 | |
| Inductive Reasoning | 3.0 | |
| Category Flexibility | 3.0 | |
| Flexibility of Closure | 3.0 | |
| Perceptual Speed | 3.0 | |
| Rate Control | 3.0 |
Essential skills
| Monitoring | 3.1 | |
| Reading Comprehension | 3.0 | |
| Active Listening | 3.0 | |
| Speaking | 3.0 | |
| Critical Thinking | 3.0 |
Skills in demand
Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.
Tools & technology
| Example | Category | |
|---|---|---|
| Autodesk AutoCAD | Computer aided design CAD software | Hot technology |
| Microsoft Excel | Spreadsheet software | Hot technology |
| Microsoft Office software | Office suite software | Hot technology |
| Microsoft Outlook | Electronic mail software | Hot technology |
| Microsoft Windows | Operating system software | Hot technology |
| Microsoft Word | Word processing software | Hot technology |
| SAP software | Enterprise resource planning ERP software | Hot technology |
| Email software | Electronic mail software |
Work context
How characteristic each condition is of the job, on O*NET's 1–5 context scale (higher = more present in day-to-day work). Each condition links to how it varies across all occupations.
How to get in
- Job zone
- Zone 2 — Job Zone 1-2: Very Little to Some Preparation Needed
- Education
- Usually requires a high school diploma or GED, though some occupations may not.
- Typical entry-level education
- High school diploma or equivalent · BLS, the typical path — not a requirement
- Related experience
- Some occupations may need little or no previous experience; others require several months to a year of experience. For example, landscaping and groundskeeping workers might require very little training or previous experience, while agricultural equipment operators can benefit from on-the job training.
- Preparation level
- SVP (Below 6.0) — total schooling plus on-the-job experience.
What to study: Precision Production . Fields of study crosswalked to this occupation (NCES CIP–SOC), not a requirement.
Education of current workers
Share of people in this occupation at each level of education.
| High School Diploma | 81.7% | |
| Post-Secondary Certificate | 7.6% | |
| Some College Courses | 5.6% | |
| Associate's Degree (or other 2-year degree) | 3.9% | |
| Less than a High School Diploma | 1.1% |
Interests & work styles
The interests and personal qualities O*NET associates with people who do this work.
Career interests (Holland / RIASEC)
| Realistic | 7.0 | |
| Conventional | 4.6 | |
| Investigative | 2.3 |
Interest areas
| Mechanics/Electronics | 4.9 | |
| Physical/Manual Labor | 4.3 | |
| Engineering | 3.5 | |
| Mathematics/Statistics | 2.2 | |
| Transportation/Machine Operation | 2.0 | |
| Information Technology | 1.6 | |
| Construction/Woodwork | 1.6 | |
| Teaching/Education | 1.3 | |
| Accounting | 1.2 | |
| Physical Science | 1.2 |
Work styles
| Attention to Detail | 2.6 | |
| Dependability | 2.5 | |
| Cautiousness | 2.0 |
Wages & employment
U.S. · annual wages (BLS OEWS)
| 10th percentile | $34,130 |
| 25th percentile | $38,320 |
| Median (50th) | $46,060 |
| 75th percentile | $56,220 |
| 90th percentile | $72,850 |
| People employed | 129,850 |
Industries that employ this occupation
Where these workers are employed, by number of jobs (national, BLS OEWS). Pay shown is the occupation's national median, not industry-specific.
| Industry | Workers | National median pay |
|---|---|---|
| Manufacturing · Sector | 105,600 | $47,550 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 16,530 | $36,200 |
| Temporary Help Services · National industry | 15,530 | $36,210 |
| Wholesale Trade · Sector | 4,670 | $46,590 |
| Machine Shops · National industry | 2,730 | $44,360 |
| Professional, Scientific, and Technical Services · Sector | 740 | $48,150 |
| Health Care and Social Assistance · Sector | 340 | $29,100 |
| Transportation and Warehousing · Sector | 260 | $49,850 |
| Engineering Services · National industry | 250 | $56,870 |
| Management of Companies and Enterprises · Sector | 250 | $64,310 |
| Construction · Sector | 140 | $54,500 |
| Plumbing, Heating, and Air-Conditioning Contractors · National industry | 110 | $45,850 |
Where this work is most concentrated
Industries where this occupation is far more common than in the economy as a whole. The location quotient is how many times more concentrated it is here (a value of 5 means five times its economy-wide share).
| Industry | Concentration | Workers |
|---|---|---|
| Machine Shops · National industry | 12.48× | 2,730 |
| Manufacturing · Sector | 9.82× | 105,600 |
| Temporary Help Services · National industry | 6.96× | 15,530 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 2.17× | 16,530 |
| Wholesale Trade · Sector | 0.92× | 4,670 |
| Engineering Services · National industry | 0.26× | 250 |
| Management of Companies and Enterprises · Sector | 0.11× | 250 |
| Plumbing, Heating, and Air-Conditioning Contractors · National industry | 0.1× | 110 |
Related occupations
Part of the Advanced Manufacturing career cluster.
- Lathe and Turning Machine Tool Setters, Operators, and Tenders, Metal and Plastic · 51-4034.00 Compare →
- Milling and Planing Machine Setters, Operators, and Tenders, Metal and Plastic · 51-4035.00 Compare →
- Grinding, Lapping, Polishing, and Buffing Machine Tool Setters, Operators, and Tenders, Metal and Plastic · 51-4033.00 Compare →
- Tool Grinders, Filers, and Sharpeners · 51-4194.00 Compare →
- Woodworking Machine Setters, Operators, and Tenders, Except Sawing · 51-7042.00 Compare →
- Cutting, Punching, and Press Machine Setters, Operators, and Tenders, Metal and Plastic · 51-4031.00 Compare →
- Cutting and Slicing Machine Setters, Operators, and Tenders · 51-9032.00 Compare →
- Industrial Machinery Mechanics · 49-9041.00 Compare →
- Computer Numerically Controlled Tool Operators · 51-9161.00 Compare →
- Rolling Machine Setters, Operators, and Tenders, Metal and Plastic · 51-4023.00 Compare →
- Tool and Die Makers · 51-4111.00 Compare →
- Grinding and Polishing Workers, Hand · 51-9022.00 Compare →
Side-by-side comparisons place two occupations’ pay, preparation, skills, and AI exposure on the same page — same data, same scale, no forecast.
What you can do with this
Options the data surfaces for Multiple Machine Tool Setters, Operators, and Tenders, Metal and Plastic — not advice or a forecast. Each is a real cross-link you can follow into the evidence.
Skills that travel
Capabilities this work builds that are used across many other occupations.
Adjacent roles
Occupations O*NET rates as related — the nearby moves on the map.
- Lathe and Turning Machine Tool Setters, Operators, and Tenders, Metal and Plastic Compare →
- Milling and Planing Machine Setters, Operators, and Tenders, Metal and Plastic Compare →
- Grinding, Lapping, Polishing, and Buffing Machine Tool Setters, Operators, and Tenders, Metal and Plastic Compare →
- Tool Grinders, Filers, and Sharpeners Compare →
- Woodworking Machine Setters, Operators, and Tenders, Except Sawing Compare →
Paths in
How people typically prepare for this work.
- High school diploma or equivalent · typical entry level
- Job zone 2 — Job Zone 1-2: Very Little to Some Preparation Needed · preparation tier
- Precision Production · field of study
Zoom out
On the global GenAI exposure gradient this work sits around the 28th percentile of 427 international occupations.
Write a report on thisheadline · factoids · citation
Multiple Machine Tool Setters, Operators, and Tenders, Metal and Plastic show 28th-percentile AI task overlap — and about 12,800 annual U.S. openings
- Multiple Machine Tool Setters, Operators, and Tenders, Metal and Plastic rank in the 28th percentile (Low band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated.Eloundou et al. (GPTs are GPTs) + Felten AIOE
- The occupation is projected to see about 12,800 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI.BLS Employment Projections 2024–34
- BLS projects employment to be declining (-0.5%) from 2024 to 2034.BLS Employment Projections 2024–34
- Median annual pay is $46,060, across about 129,850 U.S. workers.BLS OEWS (May 2024)
- Of the AI use actually observed for this work, 19% looks like augmentation (drafting, iterating, checking) rather than hands-off automation — from a Claude.ai usage sample, not a census.2026-01-15-v4-plus-2025-03-27-v2
Copy the whole kit
Multiple Machine Tool Setters, Operators, and Tenders, Metal and Plastic show 28th-percentile AI task overlap — and about 12,800 annual U.S. openings • Multiple Machine Tool Setters, Operators, and Tenders, Metal and Plastic rank in the 28th percentile (Low band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated. (Eloundou et al. (GPTs are GPTs) + Felten AIOE) • The occupation is projected to see about 12,800 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI. (BLS Employment Projections 2024–34) • BLS projects employment to be declining (-0.5%) from 2024 to 2034. (BLS Employment Projections 2024–34) • Median annual pay is $46,060, across about 129,850 U.S. workers. (BLS OEWS (May 2024)) • Of the AI use actually observed for this work, 19% looks like augmentation (drafting, iterating, checking) rather than hands-off automation — from a Claude.ai usage sample, not a census. (2026-01-15-v4-plus-2025-03-27-v2) Source: Singulariki — "Multiple Machine Tool Setters, Operators, and Tenders, Metal and Plastic". https://singulariki.com/roles/role-51-4081-00 Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.
AssetsShare imageMethodology & sourcesPress & newsroomThe newsroom
Every line is built only from figures this page already shows and cites. AI task overlap means what today's AI can attempt — not automation, job loss, or a forecast.
Sources for this page
Every figure above traces to a named public dataset and the exact release below — not hand-written opinion. See the full methodology for what each measure does and does not mean.
- O*NET 30.3 U.S. Department of Labor / National Center for O*NET Development
- BLS Occupational Employment and Wage Statistics (OEWS) May 2024 U.S. Bureau of Labor Statistics
- BLS Employment Projections 2024–2034 U.S. Bureau of Labor Statistics
- Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27) Anthropic
- Microsoft “Working with AI” working-with-ai Microsoft Research
- “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130 OpenAI / academic
- AI Occupational Exposure (AIOE) Felten, Raj & Seamans academic
- ILO / Gmyrek et al. GenAI exposure gradient 2025 International Labour Organization
- IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022 Institute for Structural Research (IBS)
- Frey & Osborne (2013) frey-osborne-automation academic
- Dingel & Neiman (2020) dingel-neiman-workathome academic
Data compiled June 2, 2026. Figures are estimates, not advice.
Cite this page
Plain
Singulariki. "Multiple Machine Tool Setters, Operators, and Tenders, Metal and Plastic." Singulariki: a source-backed encyclopedia of work. Built from O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026. https://singulariki.com/roles/role-51-4081-00
APA
Singulariki. (2026). Multiple Machine Tool Setters, Operators, and Tenders, Metal and Plastic. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-51-4081-00
BibTeX
@misc{singulariki-role-51-4081-00,
title = {Multiple Machine Tool Setters, Operators, and Tenders, Metal and Plastic},
author = {{Singulariki}},
year = {2026},
note = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026},
url = {https://singulariki.com/roles/role-51-4081-00}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.