Participatory assessment of Aklak (grizzly bear) abundance and distribution in the Kivalliq Region, Nunavut, and Northern Manitoba
Bibliographic record
Abstract
Due to multiple factors, the grizzly bears population distribution and range in both the Kivalliq regions of Nunavut and Northern Manitoba is changing. In this project, 152 observation of grizzly bears records in Manitoba, and 204 harvest records in the Nunavut was collected and adopted. The study area including the Hudson Bay, Kivalliq regions of Nunavut and Northern Manitoba will be reclassify to ten habitat types to find out the distribution of grizzly bears in different habitats. It is found that grizzly bears are more likely to be observed near water (the Hudson Bay and Nelson River) and open areas compared to forest areas. In another hand, the kill sites are relatively scattered and not concentrated on the shore. In terms of habitat type, 97.36 % of hunting occurs in Lichen-moss tundra, water and unvegetated area, which as observational data suggests, is that grizzly bears are more frequently active in open habitats, sea ice and near water, and less often in forest habitats. However, it is undeniable that the factor of grizzly bears is more difficult to be observe or hunted in forest areas. The observation in different regions of study area is uneven, areas with high human activity have more chance of sighting grizzly bears, which leads to bias in our data. Understanding the distribution of grizzly bears in the different habitats of the Kivalliq region and adjacent Northern Manitoba can help people assess the real status of grizzly bears in the area, provide data support for the grizzly bears conservation, inform co-management policies and practices on the harvest of grizzly bears.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".