Inuvialuit knowledge of Pacific salmon range expansion in the western Canadian Arctic
Bibliographic record
Abstract
Rapid climate change is altering Arctic ecosystems and significantly affecting the livelihoods and cultural traditions of Arctic Indigenous peoples. In the Inuvialuit Settlement Region (ISR), growing evidence suggests that climate change is altering marine environments. In this project we recorded and synthesized Inuvialuit knowledge of Pacific salmon. We used methods that are emergent in fisheries science to combine interview information with voluntary harvest data and better understand changes to salmon in the Arctic. We conducted 53 interviews with Inuvialuit fishers about the history of Pacific salmon harvest, how it has changed in recent decades, and concurrent changes to local environments and fish species. Our interviews show that historical, incidental salmon harvest in the ISR ranged from infrequent to common among western communities, but was rare or unprecedented among eastern communities. Participants in all six communities reported a recent increase in salmon harvest and attributed this shift to regional environmental change. Fishers were concerned that salmon would negatively affect their cultural traditions and preferred fish species. Given uncertainty about the effects of salmon on local fisheries, research on salmon in the Arctic, the likelihood of their establishment, and their potential to provide subsidies to Arctic freshwater ecosystems is vital.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".