Spatial dimension and exploitation dynamics of local fishing grounds by fishers targeting several flatfish species
Bibliographic record
Abstract
Patch exploitation dynamics, based on individual tow data, provided new insights into the fishing behavior of mixed fisheries. Fishing grounds were determined and their geometry quantified based on the proximity of tow positions. Tows were classified as being part of either searching, sampling, or exploitation behavior based on the intertow distance. Fishers can detect patches of flatfish on a scale of ∼25 nautical miles2. Catch rate during exploitation was 24%–36% above the catch rate while searching, and decreased at a rate of 20%·day–1. Once a patch was found, exploitation occurred until the catch rate dropped below a threshold level. The optimal giving-up catch rate was estimated based on the observed search time, catch rate decline, and range of fishing ground quality. The observed giving-up catch rate was 6%–11% higher than predicted by the marginal value theorem. The discrepancy between the observed and predicted patch leaving decision was consistent with the bias expected in an individual transferable quota (ITQ) management system. Our results provide a basis for interpreting vessel monitoring system (VMS) data and studying the interaction among fishers and between fishers and their resources at the appropriate time and spatial scale.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".