Indigenous-led analysis of important subsistence species response to resource extraction
Bibliographic record
Abstract
Subsistence hunting, or “country food,” is essential for Indigenous Peoples who face high food insecurity and is critical for Indigenous Food Sovereignty. For many First Nations of Canada, subsistence hunting is also inextricably linked to traditional conservation practices, as hunting is an important way of engaging with nature. In the boreal of Canada, large game such as moose (Alces alces) are a primary source of protein for many First Nations. However, resource extraction, including forestry practices and oil and gas extraction, has shifted large game distributions and affected the availability and abundance of food resources. Here, we used remote camera trap data and generalized linear models to evaluate moose habitat use and spatial-numerical response to possible stressors in north-central Alberta, including fire, harvest, oil and gas extraction, and other disturbances. We also examined the effects of human-caused stressors on habitat use by sex and age class data. The proportion of various land cover types and human land use for resource extraction were important in moose habitat use. Overall, adult moose avoided burned areas and grasslands. Notably, male, female, and young moose all used habitat differently and at different spatial scales. However, young moose (with their mothers) strongly selected natural forest disturbances such as burned areas but avoided human-created disturbances such as petroleum exploration “seismic” lines. Female moose with young attempting to maximize forage opportunities do not use human-disturbed forests in the same ways they use naturally disturbed areas. This also aligns with observations from Indigenous communities, which have linked human disturbance to declines in moose densities and displacement from traditional hunting grounds. Understanding and predicting shifts in large game distributions is critical to supporting Indigenous Food Sovereignty and identifying where industries operating on First Nations lands can better engage responsibly with First Nations.
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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 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".