Comparative Recruitment Dynamics of Alewife and Bloater in Lakes Michigan and Huron
Bibliographic record
Abstract
Abstract The predictive power of recruitment models often relies on the identification and quantification of external variables, in addition to stock size. In theory, the identification of climatic, biotic, or demographic influences on reproductive success assists fisheries management by identifying factors that have a direct and reproducible influence on the population dynamics of a target species. More often, models are constructed as one‐time studies of a single population whose results are not revisited when further data become available. Here, we present results from stock recruitment models for Alewife Alosa pseudoharengus and Bloater Coregonus hoyi in Lakes Michigan and Huron. The factors that explain variation in Bloater recruitment were remarkably consistent across populations and with previous studies that found Bloater recruitment to be linked to population demographic patterns in Lake Michigan. Conversely, our models were poor predictors of Alewife recruitment in Lake Huron but did show some agreement with previously published models from Lake Michigan. Overall, our results suggest that external predictors of fish recruitment are difficult to discern using traditional fisheries models, and reproducing the results from previous studies may be difficult particularly at low population sizes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".