Not assessed
unknown
sources: BLS OES (approx) · O*NET
0 sources
4 core tasks shown
Rock Splitters, Quarry High exposure: 0 of 4 core tasks show high task automation potential : exposure 50/100 (not assessed).
No model has published evidence mapped to this occupation yet - the score comes from generic task assessment.
Score breakdown
Why this score?
Every component is stored with the score row and can be recomputed from the evidence table.
Methodology - · score - · input hash -
Strength of current models on the capabilities this job's tasks require.
Task-level exposure, weighted by task importance. A component of the headline, not the headline itself.
Evidence that AI is actually being used for this work.
Observed changes in postings, employment or earnings.
Quantity and source quality of the evidence behind the score.
0 evidence item(s) contribute. Calculated -.
Overview
Separate blocks of rough dimension stone from quarry mass using jackhammers, wedges, or chop saws.
Employment
-
approx. BLS OES
Median wage
-
approx. BLS OES
Tasks tracked
4
linked to this occupation
Employment and wage values are approximate reference figures from the BLS Occupational Employment and Wage Statistics programme, stored with an approximation flag. Verify against the O*NET record and BLS before quoting them.
Tasks
Task-level automation exposure
Core tasks only. Exposure describes how much of the task output current AI can produce or substantially assist - not whether the task disappears. Secondary shared-baseline edges are hidden until per-occupation curation is complete.
core task · importance 3.8/5
core task · importance 3.7/5
core task · importance 3.7/5
core task · importance 3.4/5
Evidence
Evidence trail
0 attributed item(s). Confidence reflects study design: randomised and production deployments score higher than platform observations, which score higher than projections.
No occupation-specific evidence has been mapped yet. This occupation is scored from generic task assessment only - a genuinely weaker basis, and the reason its score is marked low-confidence.
Historical trend
Exposure over time
Backcast series: recomputed at quarterly checkpoints using only the models and evidence published by each date.
No series yet
This chart fills in once the first snapshot is recorded.
Reconstruction, not measurement. 0 observed daily snapshot(s) exist for this occupation; the earlier part of the curve is derived by applying the current methodology to the model releases and published evidence available at each past date.
Recent events
Signals affecting this occupation
No events match this filter yet. New verified events appear here as analysis completes.
Related
Related Occupations
Occupations sharing major SOC group (47) with Rock Splitters, Quarry.
faq
Frequently Asked Questions
Common questions regarding Rock Splitters, Quarry and AI exposure.
Will AI replace Rock Splitters, Quarry?
Current AI models show an exposure score of 50/100 for Rock Splitters, Quarry. This measures technical capability to assist or automate specific tasks rather than predicted immediate job loss. Moderate exposure reflects assistance in routine information-handling while core responsibilities require human judgment.
Which tasks in Rock Splitters, Quarry have the highest AI exposure?
The most exposed tasks for Rock Splitters, Quarry include "Locate grain line patterns to determine how rocks will split when cut." (50% exposure) and "Remove pieces of stone from larger masses, using jackhammers, wedges, and other tools." (50% exposure).
What aspects of Rock Splitters, Quarry can AI not do yet?
Tasks with the lowest AI exposure for Rock Splitters, Quarry include "Mark dimensions or outlines on stone prior to cutting, using rules and chalk lines." (50% exposure) and "Insert wedges and feathers into holes, and drive wedges with sledgehammers to split stone sections from masses." (50% exposure). These responsibilities typically require physical dexterity, licensed human accountability, or complex interpersonal navigation.
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Notice: All ratings shown are AI-estimated technical exposure scores measuring technical capabilities across O*NET task statements, not predicted job loss. Contains information from O*NET 31.0 by the U.S. Department of Labor, ETA, used under CC BY 4.0.