Recruitment forecasting using indices of young-of-the-year Pacific herring (Clupea pallasi) abundance in the Strait of Georgia (BC)
Bibliographic record
Abstract
Abstract Schweigert, J. F., Hay, D. E., Therriault, T. W., Thompson, M., and Haegele, C. W. 2009. Recruitment forecasting using indices of young-of-the-year Pacific herring (Clupea pallasi) abundance in the Strait of Georgia (BC). – ICES Journal of Marine Science, 66: 1681–1687. Within the Strait of Georgia (BC, Canada), recruitment of Pacific herring (Clupea pallasi) to the spawning stock at age 3 can be highly variable, and this component may compose a major portion of the spawning-stock biomass. Therefore, a reliable method of forecasting recruitment strength would be useful for determining total allowable catches for the fishery. We developed an empirical approach to forecasting recruitment from young-of-the-year (YOY) surveys using purse-seine sampling in late September and evaluate its predictive capability for estimating the relative size of a year class before it enters the fishery. For each year, we compared YOY catches-by-weight with the number of age-3 recruits derived from subsequent catch-at-age analyses. The relationship is positive but not statistically significant because of considerable annual variation in the estimates. However, it is worth noting that in years when YOY herring were least abundant, the resulting cohort also was low. Consequently, although the relationship may not be sufficiently precise for accurate recruitment forecasting, it can be used by fishery management for the qualitative evaluation of the likelihood of strong or weak returns in future seasons when setting quotas for the fishery.
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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 | 0.001 | 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) | 0.000 | 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".