An updated assessment of human activities, the environment, and freshwater fish biodiversity in Canada
Bibliographic record
Abstract
Changes in resource development and expansions of urban centres suggest that the intensity and types of anthropogenic stressors affecting Canada’s watersheds are changing. Chu et al. (2003) integrated indices of freshwater fish biodiversity, environmental conditions, and anthropogenic stress to identify priority watersheds for conservation and management. Here, we update those indices using recent climate and census data to assess changes through time. We also applied different conservation and management scenarios to evaluate the robustness of our prioritization approach. Between time periods, the environmental and stress indices expanded northward because of warmer temperatures at higher latitudes and more intense anthropogenic stress in the northern regions of the provinces. Conservation priorities increased in northern British Columbia, Alberta, and Ontario but decreased in southern British Columbia, Saskatchewan, and south-central Quebec. Under multiple scenarios, conservation priorities were consistently highest in British Columbia, the Maritimes, southern Ontario, and southern Quebec. Future research to refine this assessment should focus on developing a nationwide georeferenced assessment of freshwater fisheries stress, quantifying spatial changes in the stressors, and evaluating the sensitivity of each index to the weighting of the individual variables. This work highlights the necessity for conservation and management strategies in Canada to keep pace with changing patterns in climate and human activities.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".