Spatiotemporal dynamics of spawning habitat distribution of American shad (<i>Alosa sapidissima</i>) in the Hudson River Estuary under multi stressors
Bibliographic record
Abstract
Diadromous fishes, known for their extensive migrations between freshwater and marine ecosystems, are highly vulnerable to environmental fluctuations and human activities, making them prone to population declines. Despite awareness of climate change impacts and habitat limitations, the remaining spawning habitat’s biogeography is understudied. The present study focuses on the Hudson River Estuary (HRE) American shad ( Alosa sapidissima) population which is experiencing historically low stock levels, as a case study to investigate the spatiotemporal distribution of its existing spawning habitat. Generalized additive models were used to investigate the effect of some environmental (e.g., temperature, river bottom type) and sampling variables (e.g., sampling location and time) on the spatial distribution of the American shad. Our results provide compelling evidence of an optimal spawning habitat for the American shad, suggesting that environmental factors may not be the primary drivers shaping the distribution of their spawning grounds. The significant relationships between the distribution of spawning habitat and spawning stock biomass indicates that factors beyond the HRE are likely to play the most significant roles in the shad population.
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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 | 0.000 |
| 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 | 0.000 | 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".