The current status of operational oceanography and its integration in fishery resource stock assessments in the Newfoundland Region of Atlantic Canada
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.Environmental observations and ocean climate variability indices are routinely collected and complied by fisheries laboratories in many ICES member countries throughout the North Atlantic. Variations in the physical oceanographic environment are thought to influence the abundance (recruitment, survival), and behavior (distribution, catchability) of many marine organisms and hence the management and operations of the fishing industry. Therefore, the integration of environmental information into fishery resource stock assessments for management requirements in a quantitative manner is a pressing issue and one that is receiving increasing attention. A review of preliminary efforts in the Newfoundland Region of Atlantic Canada to incorporate environmental information into fish and invertebrate stock assessments is presented. In general, variations in the oceanographic environment appear to be associated with trends in production in several marine species inferred from commercial fisheries (CPUE) and assessment surveys. Results indicate that environmental factors may be important at early life history stages, particularly for crustacean populations. Statistical models were employed to explore relationships between invertebrate production and changes in the oceanographic environment in Newfoundland waters. The results indicate that even though the uncertainty in the predictions is generally large, the information can be a valuable addition to a suite of indicators used to assess current status and future prospects for the management of a number of species of marine organisms.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".