Moose–habitat relationships: integrating local Cree native knowledge and scientific findings in northern Quebec
Bibliographic record
Abstract
Participation of aboriginal people in Canadian forestry is a requirement of sustainable management. We proposed a culturally adapted process to integrate Cree and scientific knowledge in Eeyou Istchee (northern Quebec) that could contribute to a better mutual understanding between Cree and non-Cree, and eventually favour the social acceptability of forest management strategies. We studied moose ( Alces alces L.), the Cree featured species and the main forestry issue for the past 40 years. Cree and non-Cree have culturally differing visions for the management of moose habitat. In a previous article, we documented Cree knowledge about moose–habitat relationships. Here, we evaluated some hypotheses built from Cree knowledge by studying the behaviour of moose equipped with GPS collars. In general, results from our habitat use and selection analyses agreed with Cree observations and improved our understanding of moose–habitat relationships in northern Quebec. We jointly demonstrated the importance of mature mixedwoods, balsam fir ( Abies balsamea (L.) Mill.) stands, and riparian areas for moose in the northern black spruce ( Picea mariana (Mill.) BSP) forest. In these specific areas, management approaches other than clear-cutting should be developed to preserve moose habitat quality. Such an alternative would potentially be more acceptable for the Cree people.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".