Assessing the climate change vulnerability of freshwater fishes 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 threats, freshwater fishes are responding by shifting their distribution range, altering the timing of migration and spawning and through demographic processes. To mitigate the future negative consequences, managers require novel tools that provide useful information on fish vulnerability to climate change to develop appropriate responses. A trait-based vulnerability assessments methodology was applied in this study to assess the vulnerability of 7 freshwater fishes in Newfoundland and Labrador of recreational and ecological importance. Twelve vulnerability indicators were developed and 26 freshwater fish experts were consulted using an online questionnaire survey to assesses each species vulnerability. Analysis of the survey results showed one species to be high/very highly vulnerable, two species were highly vulnerable while four species were moderately vulnerable to future changes with moderate confidence from the experts. Lake trout a native species showed the highest vulnerability while was rainbow trout a non-native species showed the lowest vulnerability to future changes. The results presented in this study are significant to resource managers because findings will allow for adaptive responses targeted at each species unique vulnerability drivers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".