Moose (<i>Alces americanus</i>) habitat suitability in temperate deciduous forests based on Algonquin traditional knowledge and on a habitat suitability index
Bibliographic record
Abstract
Traditional ecological knowledge (TEK) garners increasing attention in science-based wildlife management. We used the TEK of 16 First Nation hunters from the Eagle Village Algonquin community (Quebec, Canada) to evaluate moose (Alces americanus) habitat suitability in temperate deciduous forests, compared with a habitat suitability index (HSI) model. We found moderate to strong agreement between TEK and the HSI using Cohen’s kappa (κ = 0.46–0.63). According to the Algonquin hunters, wetlands and lakes are frequented by moose to feed and to avoid temperature stress during warm summer days, something not taken into account by the HSI. Algonquin hunters also mentioned that unproductive areas are actively frequented by moose in the summer and during the rutting period, although they have a low weight in the HSI calculation. Also according to Algonquin hunters, mature coniferous stands and large-size regenerating areas are rarely used by moose. While the moose HSI model was developed in boreal mixed and coniferous forests, we have shown that it could also be used in temperate deciduous forests. It could be improved, however, to better correspond to TEK, notably by including wetlands and lakes, increasing the weight of unproductive stands and reducing weights of mature coniferous and regenerating stands.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".