Predicting habitat to optimize sampling of Pacific sardine (Sardinops sagax)
Bibliographic record
Abstract
Abstract Zwolinski, J. P., Emmett, R. L., and Demer, D. A. 2011. Predicting habitat to optimize sampling of Pacific sardine (Sardinops sagax). – ICES Journal of Marine Science, 68: 867–879. More than 40 years after the collapse of the fishery for Pacific sardine, a renewed fishery has emerged off the west coasts of the United States and Canada. The daily egg production method (DEPM) and acoustic-trawl surveys are performed annually and, to minimize the uncertainties in the estimates, sampling effort needs to be allocated optimally. Here, based on a 12-year dataset including the presence/absence of sardine eggs and concomitant remotely sensed oceanographic variables, a probabilistic generalized additive model is developed to predict spatio-temporal distributions of habitat for the northern stock of Pacific sardine in the California Current. Significant relationships are identified between eggs and sea surface temperature, chlorophyll a concentration, and the gradient of sea surface altitude. The model accurately predicts the habitat and seasonal migration pattern of sardine, irrespective of spawning condition. The predictions of potential habitat are validated extensively by fishery landings and net-sample data from the northeast Pacific. The predicted habitat can be used to optimize the time and location of the DEPM, acoustic-trawl, and aerial surveys of sardine. The method developed and illustrated may be applicable too to studies of other stocks of sardine and other epipelagic fish in other eastern boundary, upwelling regions.
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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 | 0.001 |
| 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 | 0.001 | 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".