Individual-based yield-per-recruit analysis, with an application to the Atlantic sea scallop, <i>Placopecten</i> <i>magellanicus</i>
Bibliographic record
Abstract
There can be considerable variation in fishing mortality risk among individuals in a stock, especially for sedentary or sessile species. An individual-based per-recruit model is proposed that can systematically deal with variability in fishing mortality among recruits. These variations can alter the yield per recruit (YPR) and biomass per recruit (BPR) that would be predicted from conventional theory. Where the YPR curve is concave, standard theory will tend to overestimate YPR, whereas the opposite will be true where the YPR curve is convex. Additionally, variations in fishing mortality among individuals can create a mismatch between conventional metrics of mean fishing mortality and that required to properly apply standard per-recruit theory. The concept of "recruit-weighted" fishing mortality is introduced to deal with this problem. An application of the model to the Atlantic sea scallop, Placopecten magellanicus, is presented that demonstrates that standard per-recruit theory may often misrepresent true YPR and BPR.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".