Forecasting Fishery Performance for Northern Shrimp (<i>Pandalus borealis</i>) on the Labrador Shelf (NAFO Divisions 2HJ)
Bibliographic record
Abstract
The physical environment of the ocean is believed to have a major influence on pandalid shrimp populations and there are numerous studies that incorporate environmental variables to predict and forecast landings from the fishery and/or resource abundance.Cause-and-effect mechanisms are not clearly understood in many instances but the predictive nature of the relationships provides a potentially powerful forecasting tool.Meaningful indicators of the prospects for shrimp stocks that support valuable commercial fisheries are necessary in a comprehensive stock assessment process.In this paper, a time-series analysis is used to estimate a predictive model for standardized annual catch rates (an abundance index) in a shrimp fishing area off the mid Labrador coast (NAFO Div.2HJ).Environmental data (annual winter ice cover) are incorporated in a transfer function to improve predictions of catch rates and facilitate their forecasting.Results support the hypothesis that cold conditions, which result in more extensive ice cover, are favourable for the northern shrimp (Pandalus borealis) at early life-history stages.Predictions of annual catch rates fit the observed values well in most cases and a catch-rate forecast for several years is provided.Possible functional mechanisms are discussed.
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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".