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Agriculture & Environment · Updated Aug 2026

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.

JVS 2.0.0-phase4b
AI Exposure
53/100
Moderate

How much of this occupation’s work can be materially affected by current AI systems.

Replacement Risk
42/100
Moderate

How likely exposure is to translate into reduced human demand.

Includes provisional estimates for AI adoption pressure and labour-market resilience. How this is measured

Evidence quality
Confidence83/100
Task coverage88%

Confidence reflects task coverage, mapping and capability-evidence quality, and how much of the score rests on provisional inputs.

Task-level evidence

What is driving the score?

Occupation scores are built from the task mix—not a single prediction about a job title.

JVS 2.0.0-phase4b
TaskImportanceAI impactExposure
Evaluate log characteristics and determine grades, using established criteria.High
67
Record data about individual trees or load volumes into tally books or hand-held collection terminals.High
69
Paint identification marks of specified colors on logs to identify grades or species, using spray cans, or call out grades to log markers.High
69
Measure felled logs or loads of pulpwood to calculate volume, weight, dimensions, and marketable value, using measuring devices and conversion tables.High
66
Weigh log trucks before and after unloading, and record load weights and supplier identities.High
69
Measure log lengths and mark boles for bucking into logs, according to specifications.High
68
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.High
35
Communicate with coworkers by signals to direct log movement.High
23
Drive to sawmills, wharfs, or skids to inspect logs or pulpwood.Medium
30
Identify logs of substandard or special grade so that they can be returned to shippers, regraded, recut, or transferred for other processing.High
15
Most exposed

Where AI can do more

Routine, digitized, and highly repeatable tasks face the greatest pressure.

  1. Record data about individual trees or load volumes into tally books or hand-held collection terminals.69
  2. Paint identification marks of specified colors on logs to identify grades or species, using spray cans, or call out grades to log markers.69
  3. Weigh log trucks before and after unloading, and record load weights and supplier identities.69
  4. Measure log lengths and mark boles for bucking into logs, according to specifications.68
Hardest to automate

Where people still matter

These tasks score lowest on automation feasibility—physical presence, judgement, accountability and real-world variability all resist end-to-end automation.

  1. Identify logs of substandard or special grade so that they can be returned to shippers, regraded, recut, or transferred for other processing.01
  2. Communicate with coworkers by signals to direct log movement.02
  3. Drive to sawmills, wharfs, or skids to inspect logs or pulpwood.03
  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.04
  5. Evaluate log characteristics and determine grades, using established criteria.05
Where else this work leads

Related occupations

Occupations O*NET links to this one. Relatedness reflects shared work, not a claim that these roles are safer.

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AI risk 45

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Related work

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Beyond AI capability

Adoption and labour-market outlook

Structural factors are kept separate from raw capability so you can see what actually resists automation. Adoption pressure and labour-market resilience are still provisional models—25% of this occupation’s replacement-risk weight rests on them.

Human dependency67
Physical dependency71
Adoption pressure51
Labour-market resilience66
Methodology & sources

O*NET 30.3 occupational data interpreted through the JobsVsAI capability, automation and structural-constraint models.

Confidence83/100
CalculatedAug 21, 2026
Read methodology →