Effect of environmental conditions on juvenile recruitment of alewife (<i>Alosa pseudoharengus</i>) and blueback herring (<i>Alosa aestivalis</i>) in fresh water: a coastwide perspective
Bibliographic record
Abstract
The abundance of alewife (Alosa pseudoharengus) and blueback herring (Alosa aestivalis) has declined throughout their range, and there are increasing concerns about their conservation status. Because of their diadromous life history, variability in rates of survival in fresh water can affect overall recruitment. The objective of our study was to assess how river temperature and flow influence young of the year (YOY) river herring recruitment in the Northeast US. Observations of adult and juvenile fish in five rivers were used to construct spawner–YOY recruits models; these rivers were chosen because of the length of the time series (>15 years) and the paired observations of spawners and juveniles. An environmentally explicit stock–recruitment model explained a substantial fraction (41% to 80%) of the variance in YOY abundance, depending on river system. Our approach allowed for a preliminary discussion of potential mechanisms, which need to be further substantiated by focused field and laboratory studies. Early summer river flow and river temperature had the greatest influence, indicating the importance of conditions in nursery habitats. In certain systems, spring or fall conditions were also important determinants of survival, suggesting additional effects of the environment on spawning of adults and juvenile egress from freshwater nursery habitats.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".