The relationship among catch, fishing effort, and measures of fish stock abundance: implications in the Adriatic Sea
Bibliographic record
Abstract
Relationships among catch, fishing effort, and measures of fish stock abundance have several implications for fisheries research. In this context, spatial and seasonal aspects are of significant importance for management decisions, especially when effort regulation schemes are used. In this paper, the multispecies trawl fishery in the Northern and Central Adriatic Sea was investigated, taking into account the heterogeneous distribution of fish stocks. Two approaches are presented depending on the availability (or not) of fishery-independent indices of stock abundance. The empirical results indicate that (i) aggregation and targeting behaviours affect catches by modifying the relationship between abundance and catch per unit effort and (ii) these relationships are not homogenous across space. Data from the Adriatic Sea is still insufficient to guarantee reliable estimations. However, these preliminary results call into question management decisions being made on the basis of catch per unit effort. Furthermore, the high heterogeneity between the northern and central areas of the sea basin calls for the adoption of spatially explicit management systems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".