# Log Graders and Scalers

> 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.

- **SOC code:** 45-4023.00
- **Canonical URL:** https://singulariki.com/roles/role-45-4023-00
- **Also known as:** Log Buyer, Log Grader, Log Scaler, Scaler, Log Check Scaler, Lumber Grader, Timber Buyer, Check Scaler
- **Frame:** "AI exposure" means task overlap (how codifiable the work is), not jobs lost or a forecast. Every figure below is traced to a named public dataset.

## What this work is

**Core tasks** (O*NET):
- Evaluate log characteristics and determine grades, using established criteria.
- Record data about individual trees or load volumes into tally books or hand-held collection terminals.
- Measure felled logs or loads of pulpwood to calculate volume, weight, dimensions, and marketable value, using measuring devices and conversion tables.
- Paint identification marks of specified colors on logs to identify grades or species, using spray cans, or call out grades to log markers.
- 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.
- Identify logs of substandard or special grade so that they can be returned to shippers, regraded, recut, or transferred for other processing.
- Arrange for hauling of logs to appropriate mill sites.
- Weigh log trucks before and after unloading, and record load weights and supplier identities.
- Measure log lengths and mark boles for bucking into logs, according to specifications.
- Communicate with coworkers by signals to direct log movement.
- Drive to sawmills, wharfs, or skids to inspect logs or pulpwood.
- Saw felled trees into lengths.

**Emerging tasks** (O*NET):
- Move logs using heavy equipment such as log loaders.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Near Vision _(ability)_
- Production and Processing _(knowledge)_
- Problem Sensitivity _(ability)_
- Oral Comprehension _(ability)_
- Oral Expression _(ability)_
- Mathematics _(knowledge)_
- Active Listening _(essential_skill)_
- Critical Thinking _(essential_skill)_
- Deductive Reasoning _(ability)_
- Inductive Reasoning _(ability)_
- Category Flexibility _(ability)_
- Speaking _(essential_skill)_

**Skills in demand:**
- Mathematics _(Common Skill)_
- Inductive Reasoning _(Common Skill)_
- Deductive Reasoning _(Common Skill)_
- Critical Thinking _(Common Skill)_
- Active Listening _(Common Skill)_
- Writing _(Common Skill)_
- Speech Recognition _(Specialized Skill)_
- Microsoft Word _(Common Skill)_
- Microsoft Windows _(Common Skill)_
- Microsoft PowerPoint _(Common Skill)_
- Microsoft Outlook _(Common Skill)_
- Microsoft Excel _(Common Skill)_

**Tools & technology:**
- Microsoft Access _(hot technology)_
- Microsoft Excel _(hot technology)_
- Microsoft Office software _(hot technology)_
- Microsoft Outlook _(hot technology)_
- Microsoft PowerPoint _(hot technology)_
- Microsoft Windows _(hot technology)_
- Microsoft Word _(hot technology)_
- SAP software _(hot technology)_
- AS/400 Database
- Atterbury Consultants SuperACE/FLIPS

## AI exposure & outlook

- **AI task-overlap index:** 29th percentile (Low) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 36th percentile (Moderate) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 39th percentile (Moderate) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 14th percentile (Low) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 94th percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** no — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** -0.7% growth (Declining); 0.6k annual openings; 4.6k → 4.6k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $46,710; 3,310 employed.

## Sources

- **O*NET** (30.3) — U.S. Department of Labor / National Center for O*NET Development. https://www.onetcenter.org/database.html
- **BLS Occupational Employment and Wage Statistics (OEWS)** (May 2024) — U.S. Bureau of Labor Statistics. https://www.bls.gov/oes/
- **BLS Employment Projections** (2024–2034) — U.S. Bureau of Labor Statistics. https://www.bls.gov/emp/
- **Microsoft “Working with AI”** (working-with-ai) — Microsoft Research. https://www.microsoft.com/en-us/research/
- **“GPTs are GPTs” (Eloundou et al.)** (arXiv 2303.10130) — OpenAI / academic. https://arxiv.org/abs/2303.10130
- **AI Occupational Exposure (AIOE)** (Felten, Raj & Seamans) — academic. https://github.com/AIOE-Data/AIOE
- **Frey & Osborne (2013)** (frey-osborne-automation) — academic. https://www.oxfordmartin.ox.ac.uk/publications/the-future-of-employment/
- **Dingel & Neiman (2020)** (dingel-neiman-workathome) — academic. https://github.com/jdingel/DingelNeiman-workathome

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_Generated from Singulariki's joined dataset; data snapshot 2026-06-02T21:00:32.945303+00:00. https://singulariki.com/roles/role-45-4023-00_