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 →Log Graders and Scalers
Occupation · SOC 45-4023.00
Grade logs or estimate the marketable content or value of logs or pulpwood in sorting yards, millpond, log deck, or similar locations. Inspect logs for defects or measure logs to determine volume.
Also called: Log Buyer · Log Grader · Log Scaler · Scaler · Log Check Scaler · Lumber Grader · Timber Buyer · Check Scaler · Compounding Scaler · Deck Scaler · Deck Specialist · Decker
Job family: Farming, Fishing, and Forestry Occupations
Take this to your AI
A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch
/roles/role-45-4023-00/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.
Market signal
Independent published positions, read together — not a forecast.
- Declining employment outlook (-0.7% by 2034)
- 600 openings/yr
- Low AI exposure
- Median pay $46,710/yr
- Work is mostly on-site (not teleworkable)
- 2013 flagged this for computerization (94th); today's AI task-overlap is comparatively low (29th)
29th-percentile task overlap — yet about 600 openings a year (-0.7% projected, BLS) . 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.) Moderate | 36th | -0.5 | |
| LLM task exposure, γ (OpenAI / Eloundou) Moderate | 39th | 0.4 | |
| AI assistant applicability (Microsoft) Low | 14th | 0.1 |
OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.0), with simple added tooling (β 0.2), and including AI-powered software (γ 0.4). 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 1.0 · 94th 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 · -0.7% by 2034 |
| Projected annual openings | 600 |
| Employment 2024 → 2034 | 4,600 → 4,600 |
“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.
Log Graders and Scalers sits at the 7th percentile of 427
occupations on the global GenAI task-exposure gradient
— exposure eased from 2023 to 2025. Each dot is one occupation; the
ringed one is this work. Exposure is task overlap, not automation or jobs lost.
12% mean task exposure (2025)
7th percentile of 427 placed occupations
−2 pts shift 2023 → 2025
| International occupation (ISCO-08) | Task exposure (2025) | Most tasks fall in |
|---|---|---|
| Forestry and Related Workers · 6210 | 12% | 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.
Tasks
All 13 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.
- Evaluate log characteristics and determine grades, using established criteria. Core · importance 4.7
- Record data about individual trees or load volumes into tally books or hand-held collection terminals. Core · importance 4.6
- Measure felled logs or loads of pulpwood to calculate volume, weight, dimensions, and marketable value, using measuring devices and conversion tables. Core · importance 4.5
- Paint identification marks of specified colors on logs to identify grades or species, using spray cans, or call out grades to log markers. Core · importance 4.4
- Jab logs with metal ends of scale sticks, and inspect logs to ascertain characteristics or defects such as water damage, splits, knots, broken ends, rotten areas, twists, and curves. Core · importance 4.3
- Identify logs of substandard or special grade so that they can be returned to shippers, regraded, recut, or transferred for other processing. Core · importance 4.3
- Arrange for hauling of logs to appropriate mill sites. Supplemental · importance 4.2
- Weigh log trucks before and after unloading, and record load weights and supplier identities. Supplemental · importance 4.2
- Measure log lengths and mark boles for bucking into logs, according to specifications. Supplemental · importance 4.1
- Communicate with coworkers by signals to direct log movement. Supplemental · importance 4.1
- Drive to sawmills, wharfs, or skids to inspect logs or pulpwood. Supplemental · importance 4.0
- Saw felled trees into lengths. Supplemental · importance 3.9
- Tend conveyor chains that move logs to and from scaling stations. Supplemental
Emerging tasks
Newer responsibilities O*NET has flagged as growing for this occupation.
- Move logs using heavy equipment such as log loaders.
Work activities
- Direct material handling or moving activities.
- Evaluate log quality.
- Record agricultural or forestry inventory data.
- Mark agricultural or forestry products for identification.
- Measure physical characteristics of forestry or agricultural products.
- Drive passenger vehicles.
- Communicate with other workers to coordinate activities.
- Cut trees or logs.
Knowledge, skills & abilities
O*NET importance rating, from 1 (not important) to 5 (extremely important).
Abilities
| Near Vision | 3.9 | |
| Problem Sensitivity | 3.6 | |
| Oral Comprehension | 3.4 | |
| Oral Expression | 3.4 | |
| Deductive Reasoning | 3.3 | |
| Inductive Reasoning | 3.3 | |
| Category Flexibility | 3.3 | |
| Written Expression | 3.1 | |
| Flexibility of Closure | 3.1 | |
| Perceptual Speed | 3.1 | |
| Far Vision | 3.1 | |
| Written Comprehension | 3.0 | |
| Information Ordering | 3.0 | |
| Mathematical Reasoning | 3.0 | |
| Number Facility | 3.0 | |
| Selective Attention | 3.0 | |
| Arm-Hand Steadiness | 3.0 | |
| Manual Dexterity | 3.0 | |
| Finger Dexterity | 3.0 | |
| Control Precision | 3.0 | |
| Visual Color Discrimination | 3.0 | |
| Speech Recognition | 3.0 | |
| Speech Clarity | 3.0 | |
| Visualization | 2.9 |
Knowledge
| Production and Processing | 3.8 | |
| Mathematics | 3.3 | |
| Customer and Personal Service | 3.1 | |
| Administration and Management | 3.0 |
Essential skills
| Active Listening | 3.3 | |
| Critical Thinking | 3.3 | |
| Speaking | 3.1 | |
| Writing | 3.0 | |
| Active Learning | 3.0 | |
| Monitoring | 3.0 | |
| Reading Comprehension | 2.9 |
Transferable skills
| Coordination | 3.0 | |
| Judgment and Decision Making | 3.0 | |
| Complex Problem Solving | 2.9 | |
| Operations Monitoring | 2.9 | |
| Time Management | 2.9 |
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 | |
|---|---|---|
| Microsoft Access | Data base user interface and query 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 PowerPoint | Presentation 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 |
| AS/400 Database | Data base user interface and query software | |
| Atterbury Consultants SuperACE/FLIPS | Inventory management 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.
Education of current workers
Share of people in this occupation at each level of education.
| High School Diploma | 64.2% | |
| Some College Courses | 11.6% | |
| Less than a High School Diploma | 11.2% | |
| Post-Secondary Certificate | 11.0% | |
| Associate's Degree (or other 2-year degree) | 2.0% |
Interests & work styles
The interests and personal qualities O*NET associates with people who do this work.
Career interests (Holland / RIASEC)
| Conventional | 6.3 | |
| Realistic | 5.0 | |
| Investigative | 2.4 | |
| Enterprising | 1.8 |
Interest areas
| Physical/Manual Labor | 2.9 | |
| Nature/Outdoors | 2.7 | |
| Construction/Woodwork | 2.1 | |
| Mathematics/Statistics | 2.0 | |
| Agriculture | 1.8 | |
| Accounting | 1.8 | |
| Transportation/Machine Operation | 1.7 | |
| Engineering | 1.4 |
Work styles
| Attention to Detail | 2.5 | |
| Dependability | 2.1 | |
| Cautiousness | 1.5 | |
| Integrity | 1.5 |
Wages & employment
U.S. · annual wages (BLS OEWS)
| 10th percentile | $35,050 |
| 25th percentile | $38,390 |
| Median (50th) | $46,710 |
| 75th percentile | $56,200 |
| 90th percentile | $63,370 |
| People employed | 3,310 |
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 | 2,800 | $45,130 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 240 | $61,920 |
| Agriculture, Forestry, Fishing and Hunting · Sector | 120 | $53,900 |
| Transportation and Warehousing · Sector | 60 | $60,070 |
| Wholesale Trade · Sector | 30 | $42,500 |
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 |
|---|---|---|
| Agriculture, Forestry, Fishing and Hunting · Sector | 13.2× | 120 |
| Manufacturing · Sector | 10.22× | 2,800 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 1.24× | 240 |
Related occupations
Part of the Agriculture career cluster.
- Weighers, Measurers, Checkers, and Samplers, Recordkeeping · 43-5111.00 Compare →
- Inspectors, Testers, Sorters, Samplers, and Weighers · 51-9061.00 Compare →
- Graders and Sorters, Agricultural Products · 45-2041.00 Compare →
- Logging Equipment Operators · 45-4022.00 Compare →
- Laborers and Freight, Stock, and Material Movers, Hand · 53-7062.00 Compare →
- Milling and Planing Machine Setters, Operators, and Tenders, Metal and Plastic · 51-4035.00 Compare →
- Machine Feeders and Offbearers · 53-7063.00 Compare →
- Woodworking Machine Setters, Operators, and Tenders, Except Sawing · 51-7042.00 Compare →
- Cutting and Slicing Machine Setters, Operators, and Tenders · 51-9032.00 Compare →
- Production, Planning, and Expediting Clerks · 43-5061.00 Compare →
- Fallers · 45-4021.00 Compare →
- Crushing, Grinding, and Polishing Machine Setters, Operators, and Tenders · 51-9021.00 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 Log Graders and Scalers — 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.
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
Zoom out
On the global GenAI exposure gradient this work sits around the 7th percentile of 427 international occupations.
Write a report on thisheadline · factoids · citation
Log Graders and Scalers show 29th-percentile AI task overlap — and about 600 annual U.S. openings
- Log Graders and Scalers rank in the 29th 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 600 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 (-0.7%) from 2024 to 2034.BLS Employment Projections 2024–34
- Median annual pay is $46,710, across about 3,310 U.S. workers.BLS OEWS (May 2024)
Copy the whole kit
Log Graders and Scalers show 29th-percentile AI task overlap — and about 600 annual U.S. openings • Log Graders and Scalers rank in the 29th 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 600 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 (-0.7%) from 2024 to 2034. (BLS Employment Projections 2024–34) • Median annual pay is $46,710, across about 3,310 U.S. workers. (BLS OEWS (May 2024)) Source: Singulariki — "Log Graders and Scalers". https://singulariki.com/roles/role-45-4023-00 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
- 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. "Log Graders and Scalers." 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; 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-45-4023-00
APA
Singulariki. (2026). Log Graders and Scalers. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-45-4023-00
BibTeX
@misc{singulariki-role-45-4023-00,
title = {Log Graders and Scalers},
author = {{Singulariki}},
year = {2026},
note = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; 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-45-4023-00}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.