It takes all kinds: a composite approach to sustainable freshwater fisheries
Bibliographic record
Abstract
Freshwater fisheries face diverse, interacting stressors that threaten freshwater fish populations and global freshwater biodiversity. In Canada, freshwater fisheries are managed and conserved by various government agencies and departments (lead actors) and a supporting cast of non-governmental groups and individuals (supporting actors) whose efforts range from coordinated and synergistic to disjunct and antagonistic. This is problematic, because threats to freshwater fisheries and biodiversity are highly synergistic. In some cases, these threats are addressed by strong, combined efforts by lead and supporting actors. Here, greater capacity and resilience are achieved via a composite approach, meaning an effective combination of parts to create one whole. In other cases, efforts are less plural and/or combined, and therefore weaker. We use insights from an expert sample of freshwater fisheries practitioners to describe the supporting cast, how it varies from a critical asset to an untapped resource, and the determinants of these different outcomes. Our results are not only applicable to Canada and/or freshwater fisheries, but to other cases where environmental management and conservation involve both lead and supporting actors.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".