Synchrony of alewife, <i>Alosa pseudoharengus</i>, year-class strength in the Great Lakes region
Bibliographic record
Abstract
Fish recruitment is interannually variable and challenging to predict. Annual recruitment is often regionally synchronized among populations and identifying drivers of such synchrony may help shed light on recruitment dynamics. We investigated interannual variation of alewife Alosa pseudoharengus recruitment by estimating year-class strength for populations from three of the Laurentian Great Lakes (lakes Huron, Michigan, and Ontario) using annual assessment survey data from 1968 to 2022. We first determined the age when year-class strength was set for each dataset. We then used a mixed-modeling approach incorporating age, year-class, and sampling year to estimate year-class strength for each study lake. Thereafter, we evaluated regional synchrony in year-class strength across the three lakes and evaluated potential climatic drivers of synchrony. Our results suggest that alewife year-class strength is set by age-1. Our model-derived indices of alewife year-class strength were synchronized among the three lakes, and we identified spring-summer degree-days as a potential regional driver of synchrony. This analysis highlights the potential for strong influence of climatic conditions on fish recruitment in large freshwater systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 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) | 0.001 | 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".