Bridging native and scientific observations of snowshoe hare habitat restoration after clearcutting to set wildlife habitat management guidelines on Waswanipi Cree land
Bibliographic record
Abstract
Large-scale timber harvesting in the northern black spruce forest, on Quebec Cree territory, causes immediate loss of productive wildlife habitat for Cree hunters. Duration of this impact is key information to improve forest management. The objective here was to examine the postharvesting habitat restoration delay for snowshoe hare, a species valuable to Cree hunters, as well as a wildlife indicator of the sapling stage. A minimum threshold for vegetation development was established, at which the return of hare populations is considered acceptable by Cree hunters. To do so, an adaptive approach was used, combining Cree hunter knowledge and biological assessment. Hare populations were monitored in 36 cut blocks, ranging from 0 to 30 years after harvest. Cree hunters were interviewed to determine when a cut block becomes adequate for snaring. The combined analysis of the two knowledge sources indicated that stands that meet the threshold average 4 m in height, 6300 trees/ha in density, and are aged between 13 and 27 years. Current regulation sets this threshold at 3 m in height, regardless of cut block scale, and at 20 years postcut when considering family hunting ground scale, and thus, does not fully meet sustainable resource development objectives.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".