A statistical framework for identifying the relative importance of ecosystem processes and demographic factors on fish recruitment, with application to Atlantic herring (<i>Clupea harengus</i>) in the southern Gulf of St. Lawrence
Bibliographic record
Abstract
Despite the importance of recruitment for population dynamics and assessing stock status, limited information exists on the relative influence of various ecosystem and demographic factors on the recruitment dynamics of marine fishes. We develop a statistical framework to identify the ecosystem and demographic factors influencing the recruitment of marine fishes and facilitate improved predictions of recruitment. We demonstrate the approach by examining the relative influence of ecosystem and demographic factors on the recruitment of southern Gulf of St. Lawrence spring- and fall-spawning Atlantic herring ( Clupea harengus) stocks, highlighting the benefit of considering multiple factors to better understand recruitment trends. We found that different combinations of biological and physical ecosystem factors along with demographic factors had a significant influence on the recruitment and recruitment rate of spring- and fall-spawning herring. The study emphasizes the value of considering ecosystem characteristics when examining recruitment, provides a framework for researchers to investigate and model recruitment of other fish populations, and supports the continued development and implementation of ecosystem-based fisheries management approaches for species, such as Atlantic herring.
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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 | 0.028 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".