Does spawning stock affect recruitment of New England groundfish?
Bibliographic record
Abstract
We evaluated the effects of spawning-stock size on the recruitment abundance of 11 groundfish stocks in waters off New England. Empirical patterns of association between spawning biomass and recruitment were investigated using nonparametric approaches. For most stocks, higher recruitment occurred at high stock sizes and lower recruitment at low stock sizes. Survival ratios, indexed by recruitment per unit of spawning biomass, were higher at low stock sizes and lower at high stock sizes-as expected under density dependence. To assess assumptions about recruitment dynamics, we analyzed constant-recruitment and compensatory and overcompensatory stock-recruitment models with uncorrelated and autocorrelated error structures, using Bayesian methods of statistical inference. Although no single model was adequate for all stocks, most groundfish had better than even odds of having density-dependent dynamics, and most had better than even odds of having dynamics with uncorrelated environmental variation. While some geographic and taxonomic differences in potential productivity were apparent, all stocks exhibited compensation in survival ratio at low stock sizes, albeit with substantial variation. These results indicate that conservation measures intended to increase the spawning biomass of New England groundfish will, on average, result in higher recruitment and, eventually, in increased and more stable fishery yields.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".