Advancing an Ecosystem Approach in the Gulf of Maine
Bibliographic record
Abstract
Abstract.—This chapter summarizes contributions to a theme session of the 2009 Gulf of Maine Science Symposium held in St. Andrew’s, New Brunswick in October, 2009. The session highlighted the present status of science required to observe, interpret, and predict changes in the Gulf of Maine ecosystem in the context of strategies for regional implementation of an ecosystem approach to management (EAM). Perspectives on present ecosystem approaches to Gulf of Maine fisheries management contrast the integrated ecosystem assessment approach by the U.S. National Oceanic and Atmospheric Administration, with the more incremental advancement to EAM based on traditional fisheries management practices undertaken by Fisheries and Oceans Canada. A section on contributions from the broader research community provides perspectives on observations and different approaches to analysis, including coupled physical biological modeling as a tool for the integration, interpretation, and prediction of multidisciplinary environmental data. The Atlantic Zonal Monitoring Program has established an observing system for physical and biological characteristics of Canadian coastal waters, and NERACOOS (the Northeast Regional Association of Coastal Ocean Observing Systems) is developing infrastructure for coordination of U.S. regional observing activities. A common theme is the need for more sustained time series of critical physical and biological variables that document change, especially in nearshore, coastal, and benthic habitats. Additionally, there is a need to development and maintain bridges to transfer new research knowledge, understanding, and analysis tools to the state, provincial, and federal agencies and fisheries management councils where EAM will be implemented.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".