The Bay of Quinte: a model for large lake ecosystem management
Bibliographic record
Abstract
Ecosystemmanagementoranecosystemapproachtomanage-ment has received a lot of attention in the fisheries literatureover the past decade; however, the ecosystem approach hasbeenappliedintheGreatLakesformuchlonger(Christieetal.1986).Theecosystemapproachwasarticulatedinthe1978revisiontotheGreatLakesWaterQualityAgreementandhasbeenadrivingconceptinthemanagementoftheGreatLakes.Eventhoughtheecosystemapproachhasbeenprominentinthescienceandmanagement oftheGreatLakesformorethan30years, much of the recent literature on the approach has beenaimedatapplyingtheecosystemapproachtofisheriesmanage-ment. Here we briefly identify some of the main componentsofanecosystemapproachtomanagement(whetheritisman-agementoffisheries,habitat,orlargelakes),identifyhowsci-ence contributes to this process, and then present the Bay ofQuinte as a model of the benefits of taking an ecosystemapproach to research in support of management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".