Fleet dynamics of herring trawlerschange in gear size and implications for interpretation of catch per unit effort
Bibliographic record
Abstract
Catch per unit effort (CPUE) is widely used as an index of stock abundance. It is as widely acknowledged that CPUE can be a misleading index of abundance owing to a multitude of factors including fish behavior, fishing fleet interaction, and the increase in catchability over time caused by improvement in fishing technology. Based on information concerning the size of herring trawls manufactured in Finland since the early 1980s, an increase in fishing power of the fleet was postulated. Because we lacked direct information about the size of trawls aboard, we applied a model to estimate the changes over time. In the analysis, an analogy between fish and trawls was created by adopting the concepts and algorithms from fish stock assessment into assessment of the trawl "population", where both the total number of trawls and the size of individual trawls were being analyzed. The results indicate that the average gear size has nearly tripled in 20 years. Accepting the assumption that larger trawls are generally more effective than smaller ones, a substantial increase in fishing power has taken place. As a result, sequential population models calibrated with CPUE data will be severely biased as well.
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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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".