Bibliographic record
Abstract
are writing in response to the call for proposals relative to the Atlantic Herring Fishery Catch Monitoring Program. To say the least, this somewhat unusual request for specific input from herring fishery "stakeholders " appears to duplicate efforts undertaken during the Amendment 4 scoping process. However, these companies and vessels engaged in the Atlantic herring fishery (and many of which are similarly involved in the Atlantic mackerel fishery) will reiterate and expand upon their position on monitoring of this fishery expressed during the scoping process. We are also attaching a report from Paul Starr, a fisheries assessment scientist formerly with the Canadian Department of Fisheries and Oceans, who has designed monitoring programs for a variety of fisheries in Canada and New Zealand. His curriculum vitae is also attached. This letter incorporates many of Mr. Starr's thoughts and recommendations. EXECUTIVE SUMMARY The Council has just this year completed and implemented the Standardized Bycatch Reporting Methodology Omnibus Amendment ("SBRM Amendment"), a major rulemaking designed to address comprehensively the deployment of observers necessary to characterize precisely and accurately bycatch in all managed fisheries. As a result of this process, the number
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.955 | 0.939 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".