Advancing an Ecosystem Approach in the Gulf of Maine
Bibliographic record
Abstract
Abstract.—The Gulf of Maine (GoM) may have defined borders to some, but to the Canadian fishing industry, it carries a flow of larvae, nutrients, and other resources that help sustain the fishery from Georges Bank to the West Scotian Slope to the Bay of Fundy and all points in between. The GoM provides a source of wealth to people and communities, as well as supplying what may be one of the last natural foods on the planet. The fishing industry has been using the GoM for centuries, yet it is only recently that monitoring and data gathering has been taking place. In my opinion, we can extract much more value from the fisheries than we presently do. If the fisheries resource of the GoM is not delivering its full potential, who is ultimately responsible and accountable? In the past decade, transboundary groundfish resources from Georges Bank have been successfully managed through the Transboundary Management Guidance Committee. We can improve decision making even further in a greater ecosystem context, recognizing that decisions have to be made with the information available. An ecosystem approach to fisheries proposes a pragmatic view based on assessing the risk of not meeting agreed objectives.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".