Applying the ideal free distribution to the movement of a highly mobile gillnet fishery for Pacific salmon
Bibliographic record
Abstract
Ecological models are used to understand where fishing vessels operate, improving fisheries management success. The ideal free distribution (IFD) predicts equalization of catch across locations in response to distributions of fish and competing vessels. We applied the IFD as a null model to investigate the movement and catch per vessel (CPUE) of the sockeye salmon drift gillnet fleet in Bristol Bay, AK, from 1980 to 2019. The IFD prediction of equal CPUE across areas was not supported, so we explored violations of two assumptions of the theory. We categorized each vessel according to its mobility, highlighting the “free to move” assumption, and found that when we removed nonmobile vessels, some districts had fewer vessels than predicted from their high CPUE. We examined the “equal competitive ability” assumption by calculating the average CPUE, relative to success of other vessels. The underutilization of profitable districts persisted. Overall, the fleet underused high CPUE areas when considering differences in mobility and relative fishing success, indicating that unmeasured safety concerns, travel costs, knowledge of fishing grounds, or other factors could be preventing an IFD.
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".