Inuit knowledge of mammal distribution in Nunavut: Applications in wildlife management and risk assessment
Bibliographic record
Abstract
Recovery strategies for species at risk can change harvesting quotas, directly affecting Inuit families and communities in Nunavut. Inuit knowledge (IK) of the environment is rich and complex, and integration of IK into wildlife management is mandated in Nunavut, but implementation has been challenging. Two spatial databases containing Nunavut Inuit harvest information were used to explore IK of mammals. IK distribution maps and quantitative range values of extent of occurrence (EO) and area of occupancy (AO) were derived. Range values were applied using the Canadian species at risk assessment process. Outputs were compared to information from western science (WSK) and existing species designations. IK derived distribution patterns were remarkably similar to WSK patterns; range values and status designations were not. It was concluded that there are major challenges within current risk assessment processes. IK data can contribute substantially to there and other wildlife management programs, leading to better decision making.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".