AI exposure: 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.
Reading this score
computedAt 15.4% of weighted task load, Log Graders and Scalers sits at the 24th percentile, below the point where a job's centre of gravity has moved. 72.5% of what this role does is untouched, meaning current systems cannot produce that work at all, whatever the commercial incentive.
What holds the line here is embodiment. Across this occupation's 12 tasks it averages 2.58 out of 3, the highest of the five friction dimensions. In plain terms, the work has to happen in physical space. A language model cannot move matter. Until the robotics to do this work is both good enough and cheap enough to deploy widely, capability in software does not reach it.
The most exposed thing this job does is Record data about individual trees or load volumes into tally books or hand-held collection..., at 50.0%. The least is Saw felled trees into lengths, at 0.0%. A gap of 50.0% between two parts of the same job is the reason this index publishes at task level. An occupation-wide number would have hidden both.
Within farming, fishing and forestry occupations, this one is more exposed than most. The median across the 12 roles in the group is 9.0%, and only 4 of them score higher than this. Occupational families are not uniform, and the spread inside them is often wider than the gap between them.
What would move this score. Of 12 tasks, 4 are currently banded exposed, 0 assisted and 8 untouched. For that distribution to shift materially would take robotics cheap and reliable enough to deploy at scale, not a better language model. The score is re-computed every quarter against a fresh capability reference, and the change is published rather than quietly applied.
Where the score comes from
judgedEvery task is scored through the standardised work activities it maps to. These are this occupation’s averages on the six rubric dimensions. Capability is what AI can do; the other five are what stands in the way.
| Dimension | Mean | Scale |
|---|---|---|
| Capability | 1.17 | 0-4 |
| Embodiment | 2.58 | 0-3 |
| Presence | 0.67 | 0-3 |
| Accountability | 0.79 | 0-3 |
| Context | 1.50 | 0-3 |
| Verification cost | 1.25 | 0-3 |
Task by task
12 tasks, O*NET 31.0| Task | Exposed | Assisted | Untouched | Importance | Band |
|---|---|---|---|---|---|
| Record data about individual trees or load volumes into tally books or hand-held collection terminals. | 50.0% | 25.0% | 25.0% | 4.57 | exposed |
| Weigh log trucks before and after unloading, and record load weights and supplier identities. | 50.0% | 25.0% | 25.0% | 4.24 | exposed |
| Arrange for hauling of logs to appropriate mill sites. | 30.0% | 20.0% | 50.0% | 4.24 | exposed |
| Communicate with coworkers by signals to direct log movement. | 26.7% | 23.3% | 50.0% | 4.07 | exposed |
| Evaluate log characteristics and determine grades, using established criteria. | 11.7% | 13.3% | 75.0% | 4.73 | untouched |
| 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. | 11.7% | 13.3% | 75.0% | 4.30 | untouched |
| Identify logs of substandard or special grade so that they can be returned to shippers, regraded, recut, or transferred for other processing. | 11.7% | 13.3% | 75.0% | 4.28 | untouched |
| Drive to sawmills, wharfs, or skids to inspect logs or pulpwood. | 6.7% | 18.3% | 75.0% | 3.97 | untouched |
| Measure felled logs or loads of pulpwood to calculate volume, weight, dimensions, and marketable value, using measuring devices and conversion tables. | 0.0% | 0.0% | 100.0% | 4.48 | untouched |
| Paint identification marks of specified colors on logs to identify grades or species, using spray cans, or call out grades to log markers. | 0.0% | 0.0% | 100.0% | 4.40 | untouched |
| Measure log lengths and mark boles for bucking into logs, according to specifications. | 0.0% | 0.0% | 100.0% | 4.11 | untouched |
| Saw felled trees into lengths. | 0.0% | 0.0% | 100.0% | 3.92 | untouched |
Task text and importance ratings sourced from O*NET 31.0. Shares computed. The occupation score is the importance-weighted mean.
Occupations either side of this one
The four closest scores in the same occupational family, then the four closest anywhere in the index.
Read this carefully. Exposure is not displacement. A high score means current AI systems can produce this work, not that anyone will stop paying a person to do it. Adoption depends on economics, regulation and inertia that this index deliberately does not model. How the score is built.
What this means in practice
Most of this work is not reachable by current systems, so the immediate pressure is on the administrative edges of the role rather than its core: the scheduling, the reporting, the written records. That is where time is recovered.