Does increased information about reproductive potential result in better prediction of recruitment?
Bibliographic record
Abstract
The relationship between stock size and recruitment is an essential element in the understanding of the productivity of a population. However, predicting the number of recruits produced by a population has proven to be a difficult challenge. This may in part be a result of poor estimation of reproductive potential (RP). We determined if including increased information on reproductive biology in indices of RP results in better predictions of recruitment. We investigated some of the conditions that lead to better (or worse) recruitment prediction when more biologically complex indices of RP are used. Data from four populations in the Northwest Atlantic were examined: southern Grand Bank (NAFO Division 3NO) cod ( Gadus morhua ), Flemish Cap (NAFO Division 3M) cod, Newfoundland (NAFO Division 3LNO) American plaice ( Hippoglossoides platessoides ), and Greenland halibut ( Reinhardtius hippoglossoides ) (NAFO Subarea 2 + Division 3KLMNO). Stock–recruit models paired with complex indices of RP gave a better estimate of recruitment in slightly more than half of the tests conducted. When there were larger trends in the reproductive biology (maturity at age, sex ratio and egg production), more complex indices of RP were more likely to provide a better estimate of recruitment.
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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 | 0.015 | 0.038 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".