A multivariate stock–recruitment function for cohorts with sympatric subclasses: application to maternal effects in rockfish (genus Sebastes)
Bibliographic record
Abstract
I present a multivariate stock–recruitment function (MSRF) for calculating recruitment when a cohort comprises sympatric subclasses. Sympatric subclasses emerge when there are closely interacting subgroups occupying a very similar niche, but whose ecology dictates distinct mortality rates. Examples include multispecies complexes of juvenile rockfish ( Sebastes spp.) in the California current and juvenile salmon ( Oncorhynchus spp.) in streams following different life history strategies. I derive an MSRF and apply it to the ecology of larval and juvenile rockfish with maternal effects. In several species of rockfish, older mothers produce superior larvae. This is called a maternal effect. For these species, larval and juvenile cohorts comprise several sympatric subclasses, each with a distinct mortality rate related to the age of their mothers. I apply this model to data for black rockfish ( Sebastes melanops ) in California and Oregon and find the recruitment estimates based on data from a declining population may overestimate productivity of a recovering population if maternal effects are neglected. The MSRF proves to be a flexible framework for studying recruitment in the presence of sympatric subclasses.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".