Using the Atlantic Zone Monitoring Program (AZMP) to develop indices of biophysical environmental variability in the context of pelagic fish stock assessments in the Gulf of St. Lawrence, Canada
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author. We present an approach integrating environmental monitoring data and fish stock assessment parameters with the goal of measuring the impacts of environmental variability on pelagic fish stock dynamics in the Gulf of St. Lawrence (GSL), Canada. Environmental data were collected by the Department of Fisheries and Oceans through the Atlantic Zone Monitoring Program (AZMP) during spatial surveys with high-frequency sampling sites. Forty variables were selected to describe longterm changes in physical environmental conditions (1971-2012), zooplankton abundance/composition and phenology (1992-2012). Principal Component Analysis (PCA) was performed to reduce the data set into composite variables describing the dominant patterns of environmental variability. PCAs revealed different modes of variability with evidence for a strong link between physical forcing and zooplankton dynamics. Generalized Additive Models (GAM) revealed strong effects of environmental variability on the recruitment strength and condition of pelagic fish stocks in the GSL. Our results highlight the importance of considering environmentally-driven variations of pelagic fish stock productivity in the stock assessment process.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".