Fifteen years of Canada’s <i>Species at Risk Act</i>: Evaluating research progress for aquatic species in the Great Lakes – St. Lawrence River basin<sup>1</sup>
Bibliographic record
Abstract
More than 15 years have passed since Canada’s Species at Risk Act was enacted. To evaluate scientific progress in support of the Act, we identified research accomplishments up to 2017 for imperilled aquatic species in the Great Lakes – St. Lawrence River basin based on recovery documents and an expert survey, spanning 1182 activities across 68 research topics for 45 fish and mussel species. Greatest progress was observed for population ecology (38% of activities with major progress) and habitat science (28%), with comparably less progress on threats (mechanisms and impacts; 19%) and recovery (threat mitigation and reintroduction; 21%). As a result of lagging progress, threat and reintroduction topics were prioritized for a Canadian Freshwater Species at Risk Research Network (SARNET; 2017–2020), which focused on addressing key knowledge gaps with novel applications. This special issue outlines the SARNET projects, which span novel field, laboratory, and analytical activities. Continued research investment into novel and existing approaches is necessary to advance scientific achievements for fishes and mussels in support of the Species at Risk Act in Canada.
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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 | 0.020 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".