Freshwater Fisheries in Canada: Historical and Contemporary Perspectives on the Resources and Their Management
Bibliographic record
Abstract
Abstract.—Maintaining the viability and sustainability of freshwater fisheries lies at the heart of the intersection between fisheries science, management, and policy. In response to changing biotic and abiotic drivers, fisheries science has advanced considerably in recent years, becoming more integrative, multi-disciplinary, and diverse. We have not only gained a better understanding of fish, ecosystems, and fisheries, but have started to bridge the gap between science, management, and policy. Despite notable successes of fisheries science and management in Canada, our fisheries face a number of threats, including climate change, invasive species, habitat degradation, regulated rivers and water demands, and overexploitation. How we address these threats will be determined in part by the way we value knowledge from diverse sources. With such uncertainty on the horizon, there is value in taking a philosophical view of the future of freshwater fisheries in Canada. Our objective is to provide an overview of risk factors relevant to Canadian freshwater fisheries from the perspectives of a diverse team of primarily early-career fisheries scientists. We integrate our prognostications to provide an outlook for the future of freshwater fisheries science, management, and policy in Canada.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".