Assessing the vulnerability of freshwater fishes to climate change in Newfoundland and Labrador
Bibliographic record
Abstract
Freshwater fish populations are rapidly declining globally due to the impacts of rapid climate change and existing non-climatic anthropogenic stressors. In response to these drivers, freshwater fishes are responding by shifting their distribution range, altering the timing of migration and spawning and through demographic processes. By 2050, the mean daily air temperature is predicted to increase by 2 to 3 degrees C in insular Newfoundland and by 3 to 4 degrees C in Labrador. Mean daily precipitation is also projected to increase in all locations, with increased intensity projected for several regions. To mitigate negative consequences of these changes, managers require analytical approaches that describe the vulnerability of fish to climate change. To address this need, the current study adopts the National Marine Fisheries Service vulnerability assessment framework to characterize the vulnerability of freshwater fishes in Newfoundland and Labrador. Twelve vulnerability indicators were developed from an extensive literature review and applied to the assessment. Experts were solicited using an online questionnaire survey and scores for exposure, sensitivity and adaptive capacity were collated and analyzed to derive a final vulnerability score and rank for each species. The analysis showed one species to be of high-very high vulnerability, two species were highly vulnerable while four species were moderately vulnerable to climate change. The result provides insight into the factors that drive vulnerability of freshwater fishes in the region, this information is significant to decision-makers and other stakeholders engaged in managing freshwater fish resources in Newfoundland and Labrador.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".