Fishery and Aquaculture Labourers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 7 task statements that define Fishery and Aquaculture Labourers (ISCO-08 9216) score an average of 0.11 on a 0–1 exposure scale — more exposed than about 4% of the 427 placed occupations. Roughly 0% of its tasks fall somewhere on the exposed part of the gradient, and the typical task lands in the Not exposed band.

Exposure is task overlap, not a verdict. A high score means a generative-AI model can do part of the content of these tasks — it says nothing about whether the work is automated, whether anyone uses AI for it today, or whether jobs are lost. The gradient is scored on the international ISCO-08 system; the rest of Singulariki is U.S. O*NET/SOC, bridged below by an approximate, many-to-many crosswalk.

How its tasks split across the gradient

Each of the 7 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Handling mooring lines during docking.”

Scores 0.15 on the 2025 scale. The task of handling mooring lines during docking is primarily physical and involves significant manual dexterity, coordination, and real-time situational awareness, similar to other tasks with physical manipulation and safety-critical requirements. For instance, tasks like "Confirming positions of unloaded and loaded general cargo" scored 0.195 and "Disconnecting and coupling metro rolling stock" scored 0.15 due to their hands-on nature and the necessity for human judgment in dynamic conditions. While Generative AI can aid with planning and communication, it lacks the capability to perform the physical aspects of docking, such as managing mooring lines. The need for precise coordination, adaptability to environmental conditions, and safety considerations during docking means a low automation potential for Generative AI. Therefore, given the constraints and abilities of AI and considering the adjusted scores of tasks with similar physical demands, an adjusted automation score of 0.15 is appropriate.

Moving fastest, 2023 → 2025

“Handling mooring lines during docking.”

Model capability on this task changed by +0.05 in two years — the gradient is not static, it is filling in.

U.S. occupations this maps to

The American O*NET/SOC roles that crosswalk to ISCO-08 9216, biggest by employment first, via the published (approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 correspondence. These are the closest U.S. matches — not an asserted one-to-one identity.

• Farmworkers, Farm, Ranch, and Aquacultural Animals

In context

Part of the 9 - Elementary occupations major group. Return to the full gradient to see how the whole group sits.

Fishery and Aquaculture Labourers sit at the 4th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Fishery and Aquaculture Labourers rank in the 4th percentile for GenAI task exposure — overlap with what generative AI can attempt, not a projection of displacement. (ILO / Gmyrek et al. (2025) GenAI exposure gradient)
• About 0% of this occupation's tasks fall into an exposed gradient band. (ILO / Gmyrek et al. (2025))
• Mean task exposure rose by 0.01 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Handling mooring lines during docking.". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Fishery and Aquaculture Labourers". https://singulariki.com/gradient/9216-fishery-and-aquaculture-labourers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.