# Fallers

> Use axes or chainsaws to fell trees using knowledge of tree characteristics and cutting techniques to control direction of fall and minimize tree damage.

- **SOC code:** 45-4021.00
- **Canonical URL:** https://singulariki.com/roles/role-45-4021-00
- **Also known as:** Logger, Timber Cutter, Timber Faller, Tree Faller, Cutter Operator, Sawyer, Tree Feller, Tree Topper
- **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):
- Stop saw engines, pull cutting bars from cuts, and run to safety as tree falls.
- Appraise trees for certain characteristics, such as twist, rot, and heavy limb growth, and gauge amount and direction of lean, to determine how to control the direction of a tree's fall with the least damage.
- Saw back-cuts, leaving sufficient sound wood to control direction of fall.
- Clear brush from work areas and escape routes, and cut saplings and other trees from direction of falls, using axes, chainsaws, or bulldozers.
- Measure felled trees and cut them into specified log lengths, using chain saws and axes.
- Assess logs after cutting to ensure that the quality and length are correct.
- Determine position, direction, and depth of cuts to be made, and placement of wedges or jacks.
- Control the direction of a tree's fall by scoring cutting lines with axes, sawing undercuts along scored lines with chainsaws, knocking slabs from cuts with single-bit axes, and driving wedges.
- Trim off the tops and limbs of trees, using chainsaws, delimbers, or axes.
- Select trees to be cut down, assessing factors such as site, terrain, and weather conditions before beginning work.
- Maintain and repair chainsaws and other equipment, cleaning, oiling, and greasing equipment, and sharpening equipment properly.
- Insert jacks or drive wedges behind saws to prevent binding of saws and to start trees falling.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Reaction Time _(ability)_
- Multilimb Coordination _(ability)_
- Control Precision _(ability)_
- Arm-Hand Steadiness _(ability)_
- Manual Dexterity _(ability)_
- Speed of Limb Movement _(ability)_
- Static Strength _(ability)_
- Operation and Control _(transferable_skill)_
- Trunk Strength _(ability)_
- Stamina _(ability)_
- Gross Body Coordination _(ability)_
- Near Vision _(ability)_

**Skills in demand:**
- Depth Perception _(Common Skill)_
- Critical Thinking _(Common Skill)_
- Visualization _(Specialized Skill)_
- Information Ordering _(Specialized Skill)_
- Microsoft Word _(Common Skill)_
- Microsoft PowerPoint _(Common Skill)_
- Microsoft Outlook _(Common Skill)_
- Microsoft Excel _(Common Skill)_
- Equipment Maintenance _(Specialized Skill)_
- Deductive Reasoning _(Common Skill)_
- Active Listening _(Common Skill)_
- Speech Recognition _(Specialized Skill)_

**Tools & technology:**
- Microsoft Excel _(hot technology)_
- Microsoft Office software _(hot technology)_
- Microsoft Outlook _(hot technology)_
- Microsoft PowerPoint _(hot technology)_
- Microsoft Word _(hot technology)_
- Assisi Compiler
- Assisi Software Assisi Inventory
- Assisi Software Assisi Manager
- Assisi Software Assisi Resource
- BCS Woodlands Software The Logger Tracker
- BCS Woodlands Software Woodlands Tracker
- ESRI ArcView

## AI exposure & outlook

- **AI task-overlap index:** 6th percentile (Low) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 2nd percentile (Low) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 9th percentile (Low) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 20th percentile (Low) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 62nd percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** no — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** -7.3% growth (Declining); 0.7k annual openings; 5.6k → 5.2k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $53,900; 4,110 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-45-4021-00_