Paths to resilience: the walleye pollock fleet uses multiple fishing strategies to buffer against environmental change in the Bering Sea
Bibliographic record
Abstract
Fishers seek to maximize profits, so when choosing where to fish, they must consider interactions among the environment, costs, and fish prices. We examined catcher vessels in the US Bering Sea fishery for walleye pollock (Gadus chalcogrammus) (2003–2015) to characterize fisher responses to environmental change (e.g., abundance and water temperature). When pollock were abundant and the water warm, the fleet fished in similar locations. When temperatures were cooler or pollock abundance declined, two fishing strategies emerged, depending on the processor where a vessel delivered. One vessel group, whose catches were more likely to become fillets, often made shorter trips, requiring less fuel and time at sea. A second vessel group, whose catches were more likely to become surimi, traveled farther from port to regions with higher catch rates but generally smaller fish. By fishing in different locations to satisfy different markets, the fleet sustained revenues and buffered against environmental change. We identify a suite of socioeconomic indicators of the impacts of ecosystem change and illustrate that a one-vessel-fits-all approach may be insufficient for assessing the resilience of fleets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".