Behavioral inferences from the statistical distribution of commercial catch: patterns of targeting in the landings of the Dutch beam trawler fleet
Bibliographic record
Abstract
The objective identification of targeting behavior in multispecies fisheries is critical to the development and evaluation of management measures. Here, we illustrate how the statistical distribution of commercial catches can provide information on species preference that is consistent with economic data but not a simple function of price. Using the Dutch beam trawl fishery from 1998 to 2003, we show that the distribution of the log10-transformed catch rates of preferred species exhibit greater negative skews than less preferred species. Furthermore, subsets of the fleet employing spatially distinct strategies generate the expected patterns in the skews of their catch distributions. A simple model is presented to illustrate a behavioral mechanism for variation in skews and identify circumstances where it could apply. As a result of this analysis we propose that (i) catch distributions should be examined by species when investigating targeting behavior and (ii) changes in error structure over time can be expected in comparisons of catch statistics such as those used to create abundance indices or estimate fishing power.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".