Approaches and research needs for advancing the protection and recovery of imperilled freshwater fishes and mussels in Canada<sup>1</sup>
Bibliographic record
Abstract
Effective conservation requires that species recovery measures are informed by rigorous scientific research. For imperilled freshwater fishes and mussels in Canada, numerous research gaps exist, in part owing to the need for specialized research methods. The Canadian Freshwater Species at Risk Research Network (SARNET) was formed and identified or implemented approaches to address current research gaps, including (1) captive experimental research populations, (2) nonlethal methods for estimating abundance and distribution, (3) nonlethal field methods to measure life-history parameters, (4) species distribution models informed by co-occurring species, (5) conservation physiology to inform habitat and threat science, (6) evidence syntheses to evaluate threats and recovery measures, (7) disease-transmission models to understand mussel–host relationships, (8) experimental mesocosms and manipulative experiments to evaluate key habitat stressors, (9) threat and hazard models for predictive applications, and (10) rigorous evaluation of surrogate species. Over a dozen threat- and recovery-focused SARNET research applications are summarized, demonstrating the value of a coordinated research program between academics and government to advance scientific research on, and to support the recovery of, imperilled freshwater species.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".