Hydrologists

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

    0.075

    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

    0.36

    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, 60% 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 3 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
Develop or modify methods for conducting hydrologic studies.

O*NET Task ID 9111

1.0
Develop computer models for hydrologic predictions.

O*NET Task ID 18617

1.0
Prepare written and oral reports describing research results, using illustrations, maps, appendices, and other information.

O*NET Task ID 18618

1.0
Study and document quantities, distribution, disposition, and development of underground and surface waters.

O*NET Task ID 9100

0.5
Coordinate and supervise the work of professional and technical staff, including research assistants, technologists, and technicians.

O*NET Task ID 9102

0.5
Prepare hydrogeologic evaluations of known or suspected hazardous waste sites and land treatment and feedlot facilities.

O*NET Task ID 9103

0.5
Design and conduct scientific hydrogeological investigations to ensure that accurate and appropriate information is available for use in water resource management decisions.

O*NET Task ID 9104

0.5
Study public water supply issues, including flood and drought risks, water quality, wastewater, and impacts on wetland habitats.

O*NET Task ID 9105

0.5
Collect and analyze water samples as part of field investigations or to validate data from automatic monitors.

O*NET Task ID 9106

0.5
Apply research findings to help minimize the environmental impacts of pollution, waterborne diseases, erosion, and sedimentation.

O*NET Task ID 9107

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