AI exposure: Logging Equipment Operators
Drive logging tractor or wheeled vehicle equipped with one or more accessories, such as bulldozer blade, frontal shear, grapple, logging arch, cable winches, hoisting rack, or crane boom, to fell tree; to skid, load, unload, or stack logs; or to pull stumps or clear brush. Includes operating stand-alone logging machines, such as log chippers.
Reading this score
computedAt 9.0% of weighted task load, Logging Equipment Operators sits at the 12th percentile, below the point where a job's centre of gravity has moved. 85.3% 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 9 tasks it averages 2.67 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 Fill out required job or shift report forms, at 66.7%. The least is Calculate total board feet, cordage, or other wood measurement units, at 0.0%. A gap of 66.7% 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 less exposed than the median of 10.8% across the group's 12 roles, with 6 scoring higher. Being in an exposed family does not make a particular job exposed, and the reverse holds too.
What would move this score. Of 9 tasks, 1 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 | 0.61 | 0-4 |
| Embodiment | 2.67 | 0-3 |
| Presence | 0.67 | 0-3 |
| Accountability | 0.94 | 0-3 |
| Context | 1.28 | 0-3 |
| Verification cost | 1.11 | 0-3 |
Task by task
9 tasks, O*NET 31.0| Task | Exposed | Assisted | Untouched | Importance | Band |
|---|---|---|---|---|---|
| Fill out required job or shift report forms. | 66.7% | 33.3% | 0.0% | 3.58 | exposed |
| Grade logs according to characteristics such as knot size and straightness, and according to established industry or company standards. | 11.7% | 13.3% | 75.0% | 4.27 | untouched |
| Inspect equipment for safety prior to use, and perform necessary basic maintenance tasks. | 6.7% | 5.8% | 87.5% | 4.54 | untouched |
| Control hydraulic tractors equipped with tree clamps and booms to lift, swing, and bunch sheared trees. | 0.0% | 0.0% | 100.0% | 4.41 | untouched |
| Drive straight or articulated tractors equipped with accessories such as bulldozer blades, grapples, logging arches, cable winches, and crane booms to skid, load, unload, or stack logs, pull stumps, or clear brush. | 0.0% | 0.0% | 100.0% | 4.14 | untouched |
| Drive crawler or wheeled tractors to drag or transport logs from felling sites to log landing areas for processing and loading. | 0.0% | 0.0% | 100.0% | 3.84 | untouched |
| Drive tractors for building or repairing logging and skid roads. | 0.0% | 0.0% | 100.0% | 3.24 | untouched |
| Drive and maneuver tractors and tree harvesters to shear the tops off of trees, cut and limb the trees, and cut the logs into desired lengths. | 0.0% | 0.0% | 100.0% | 4.24 | untouched |
| Calculate total board feet, cordage, or other wood measurement units, using conversion tables. | 0.0% | 0.0% | 100.0% | 3.46 | 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.