Response of Scotian Shelf silver hake (<i>Merluccius bilinearis</i>) to environmental variability
Bibliographic record
Abstract
Abstract Commercially‐exploited fish populations are not only shaped by fishing pressure, but also by the dynamics of their environment. By quantifying the influence of environmental variability, fisheries management advice can be improved and uncertainties reduced. To this end, we developed statistical models of the response of Scotian Shelf silver hake stock metrics to variability in zooplankton community composition and phenology over the past 18 years and in the physical environment since 1985. Dominant modes of variability in these pelagic habitat indicators were characterized using principal component analysis, and the relationships of silver hake condition, abundance, and recruitment to pelagic habitat variability were assessed using generalized additive models. Condition was largely modulated by the onset and duration of the spring bloom, which controls food availability. In contrast, adult abundance was governed by composition of the zooplankton community and bottom‐water temperature, which dictates the distribution of silver hake. Finally, recruitment was affected by both thermal conditions and food availability. Our results presented here form the basis for qualitative assessment of ecosystem attributes and the influence on silver hake stock productivity.
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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 | 0.000 | 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".