Oceanographers

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

    0.043

    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 5 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
Communicate geological findings by writing research papers, participating in conferences, or teaching geological science at universities.

O*NET Task ID 3712

1.0
Develop applied software for the analysis and interpretation of geological data.

O*NET Task ID 3716

1.0
Review environmental, historical, or technical reports and publications for accuracy.

O*NET Task ID 21176

1.0
Analyze and interpret geological, geochemical, or geophysical information from sources, such as survey data, well logs, bore holes, or aerial photos.

O*NET Task ID 3702

0.5
Prepare geological maps, cross-sectional diagrams, charts, or reports concerning mineral extraction, land use, or resource management, using results of fieldwork or laboratory research.

O*NET Task ID 3705

0.5
Locate and estimate probable natural gas, oil, or mineral ore deposits or underground water resources, using aerial photographs, charts, or research or survey results.

O*NET Task ID 3706

0.5
Assess ground or surface water movement to provide advice on issues, such as waste management, route and site selection, or the restoration of contaminated sites.

O*NET Task ID 3707

0.5
Conduct geological or geophysical studies to provide information for use in regional development, site selection, or development of public works projects.

O*NET Task ID 3709

0.5
Advise construction firms or government agencies on dam or road construction, foundation design, land use, or resource management.

O*NET Task ID 3711

0.5
Analyze and interpret geological data, using computer software.

O*NET Task ID 15224

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