Indigenous-led camera-trap research on traditional territories informs conservation decisions for resource extraction
Bibliographic record
Abstract
The resource extraction that powers global economies is often manifested in Indigenous Peoples’ territories. Indigenous Peoples living on the land are careful observers of resulting biodiversity changes, and Indigenous-led research can provide evidence to inform conservation decisions. In the Nearctic western boreal forest, landscape change from forest harvesting and petroleum extraction is intensive and extensive. A First Nations community in the Canadian oil sands co-created camera-trap research to explore observations of presumptive species declines, seeking to identify the relative contributions of different industrial sectors to changes in mammal distributions. Camera data were analyzed via generalized linear models in a model-selection approach. Multiple forestry and petroleum extraction features positively and negatively affected boreal mammal species. Pipelines had the greatest negative effect size (for wolves), whereas well sites had a large positive effect size for multiple species, suggesting the energy sector as a target for co-management. Co-created research reveals spatial relationships of disturbance, prey, and predators on Indigenous traditional territories. It provides hypotheses, tests, and interpretations unique to outside perspectives; Indigenous participation in conservation management of their territories scales up to benefit global biodiversity conservation.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".