Investigating climate change impacts on Arctic Charr (Salvelinus alpinus) in Canada and the Circumpolar Region: environmental and species interactions
Bibliographic record
Abstract
One of the greatest challenges for researchers today is understanding climate change impacts on fish populations, particularly in vulnerable ecosystems such as the Canadian Arctic. Northern fish populations will undergo thermal stress as atmospheric temperatures are projected to rise globally. Models that consider both environmental factors and species interactions can help project the future distribution of a species. This thesis investigates the climate change impacts of rising temperatures and the potential northward shift of Brook Trout (Salvelinus fontinalis) on Arctic Charr (Salvelinus alpinus), Canada’s highly valuable and northernmost fish species. Understanding the current distribution of Arctic Charr in Canada will help determine future projections based on warming temperatures and species interactions. A logistic regression model for Arctic Charr evaluated a baseline time period (1976-2005) using growing-degree day, longitude, latitude, and Brook Trout occurrences, correctly classified 93% of Arctic Charr occurrences in Canada. The distribution of Arctic Charr is projected to contract by 18% in Canada by the time period of 2051-2080 using a High Carbon scenario. The projected distributions only included known native populations of Arctic Charr and Brook Trout and excluded any deliberate or accidental human-induced introductions. The decrease in the projected distribution of Arctic Charr could be attributed to warming atmospheric temperatures that lengthen growing seasons in the Arctic. The Canadian high Arctic will provide refuge for Arctic Charr, where conservation efforts will need prioritizing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| 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".