Bibliographic record
Abstract
The Georges Bank haddock stock was last assessed as part of the GARM-III (Brooks et al. 2008). That assessment was a benchmark, and all methods that were accepted by the GARM-III review panel were used in this update. Three additional years of catch and index data are incorporated. B. Fishery Total catches of Georges Bank haddock have steadily increased in the past decade, from 8711 mt in 2000 to 25903 mt in 2010 (Table B1; Fig. 1). Catch exceeded 20000 mt for the last three years (2008-2010). On average, the US fraction of recent catch has been 30 % with Canada accounting for the remaining catch. US catch has been dominated by landings the last three years, with over 90 % of the landings due to otter trawl gear (Table B2). Longline and gillnet account for most of the remaining landings. In 2007, about 40 % of the US catch was due to discarding (Table B3), primarily undersized fish from the 2003 boomer year class. Since then, discard estimates have decreased dramatically, accounting for 1-6 % of the total catch. The number of trips
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.231 | 0.118 |
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".