A dynamic programming model of fishing strategy choice in a multispecies trawl fishery with trip limits
Bibliographic record
Abstract
Dynamic programming was used to model targeting decisions made by bottom trawling vessels in the U.S. west coast groundfish fishery, under management-imposed limits on landings of each target species (trip limits). A model of choice of assemblage (bottom rockfish (Sebastes sp.) versus deepwater Dover sole (Microstomus pacificus) complex) within a fishing trip was parameterized with data from an observer study conducted in 1988 through 1990. The model predicted that the vessel would fish the bottom rockfish strategy exclusively without limits but would switch between strategies several times under restrictive trip limits. That higher limits increased switching was consistent with actual landings from trips made by the same vessels under the same trip limit regimes, although the actual landings were more variable. Different trip limits or different market prices for the limited species changed the predicted decisions. Changing the cost of fishing each strategy, probability of a premature trip ending, tow duration, and time between tows also changed the predicted decisions, but the input parameters had to be well outside the range of values observed in the fishery.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".