Overlooked biological and economic implications of within-season fishery dynamics
Bibliographic record
Abstract
Catch equations relate fisheries catch to initial fish abundance and the applied fishing pressure. The Baranov catch equation, often simply referred to as the catch equation, is the commonest one. However, there are exactly three ways of describing seasonal progression of fishing parsimoniously with a single parameter: assume catch rate, fishing effort, or fishing mortality is constant, the last being the assumption underlying the Baranov catch equation. These assumptions imply different dynamics, and only in special cases two of these assumptions can hold true simultaneously. Whether this happens is dictated by the concentration profile (i.e., the dependence of mean fish density where fishing takes place on total stock abundance). We show that the assumed seasonal progression of fishing and the type of the concentration profile have major implications for fishery dynamics as well as biological and economic consequences of fishing, calling for increased awareness of these overlooked assumptions of fishery dynamics. However, in many cases the Baranov catch equation serves as a good approximation, even when its assumption of constant fishing mortality is violated.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".