Effect of missing values from the Canadian spring and fall surveys of NAFO Divisions 3LNO on the calculation of the TAC using the Greenland halibut HCR
Bibliographic record
Abstract
To test the impact of ignoring recent missing abundance indices for Greenland Halibut in NAFO divisions 2+3KLMNO on applying the accepted HCR for this population to provide a TAC recommendation for 2023, the impact of similar exclusions in the past is examined and found to be small. To further test of the impact of the missing 2021 index from the Canada Fall 3LNO survey, a range of pessimistic to optimistic abundance index values were assumed to assess the plausible range of impact this one value might have on the TAC computation. The range of the resultant TACs is small, and the difference of the impact of TACs at either end of this range on exploitable biomass projections for the next year is found to be negligible. Hence, it is argued, the minimalist and straightforward approach of simply ignoring the missing 2021 Canadian Fall 3LNO result in the four-survey version of the HCR agreed last year would be a defensible and appropriate approach to the required adjustment of the implementation of this HCR to provide a TAC recommendation for 2023.
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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 | 0.021 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".