Conservation Scientists

Life, Physical & Social Sciences

AI exposure

  • Data source: BLSPublished: 2026-08

    High· relative

    LowFour relative bandsVery high

    Group-level value

    Scale, basis and source

    Four relative bands (Low / Moderate / High / Very high)

    831 detailed occupations in the BLS Employment Projections table. Assigned per National Employment Matrix (NEM) code, so occupations sharing a NEM code carry the same band

    Source dataset (XLSX download)

  • Data source: AnthropicPublished: 2026-03

    0.000

    0.000Range of values carried here0.745
    Scale, basis and source

    Observed exposure index, 0–1 as published

    Mapped onto O*NET tasks

    Source dataset

  • Data source: ILOPublished: 2025

    0.38

    0.09Range of values carried here0.70

    Group-level value

    Scale, basis and source

    Generative AI exposure index, 0–1 as published

    ISCO-08 unit group — every occupation sharing the code gets this value

    Computed by this site, not published by the ILO: of the 1,012 occupations this site links to the ILO dataset, 48% score at or above this value.

    Source dataset

What kind of figure this source publishes

The BLS category is a relative rank, not an absolute level, and it is not a first-hand measurement: it groups an occupation's percentile ranks across several published studies into four bands. It is not an employment or wage forecast, not a probability of adoption, and it does not separate automation from augmentation.

Task-level exposure

Show 4 hidden tasks

Values in this tab are predicted labels, not observations. Eloundou et al. (2023) published two rating regimes — human raters and GPT-4 — and the β shown here is derived from the GPT-4 rater basis alone; the same task can take a different value under the other regime. The unit and the meaning differ from the observed shares (%) in the other tabs, so do not place them on the same axis.

TaskβE1 + 0.5 × E2
Advise land users, such as farmers or ranchers, on plans, problems, or alternative conservation solutions.

O*NET Task ID 22134

0.5
Monitor projects during or after construction to ensure projects conform to design specifications.

O*NET Task ID 22135

0.5
Visit areas affected by erosion problems to identify causes or determine solutions.

O*NET Task ID 22136

0.5
Apply principles of specialized fields of science, such as agronomy, soil science, forestry, or agriculture, to achieve conservation objectives.

O*NET Task ID 22138

0.5
Gather information from geographic information systems (GIS) databases or applications to formulate land use recommendations.

O*NET Task ID 22139

0.5
Compute design specifications for implementation of conservation practices, using survey or field information, technical guides or engineering manuals.

O*NET Task ID 22140

0.5
Participate on work teams to plan, develop, or implement programs or policies for improving environmental habitats, wetlands, or groundwater or soil resources.

O*NET Task ID 22141

0.5
Conduct fact-finding or mediation sessions among government units, landowners, or other agencies to resolve disputes.

O*NET Task ID 22142

0.5
Respond to complaints or questions on wetland jurisdiction, providing information or clarification.

O*NET Task ID 22144

0.5
Compute cost estimates of different conservation practices, based on needs of land users, maintenance requirements, or life expectancy of practices.

O*NET Task ID 22145

0.5

β = E1 + 0.5 × E2 · E1 = tasks where direct LLM access alone cuts time by at least 50%, E2 = tasks where software built on top of an LLM cuts time by at least 50%. Values take only 0 / 0.5 / 1.0.

Data sources & licenses — O*NET®, Eloundou et al. (2023): see full notices on the Credits page

Occupation information