Wildlife mitigation burn monitoring program at Teck Coal Limited - Fording River Operations
Bibliographic record
Abstract
In 1997, Fording River Operations (FRO) implemented a prescribed–burn program with the objective of mitigating the effects of ungulate habitat loss due to mine expansion. Fording River personnel, in consultation with the British Columbia Ministry of Water, Land and Air Protection, identified six mitigation burn areas totaling 460.0 hectares (ha) of habitat improvement. The objective of the mitigation burns was to increase wildlife habitat suitability and to provide winter habitat for elk and moose. Each of the treatment areas was subjected to a similar prescribed burn. A monitoring program was established in 1998 to evaluate the results of the prescribed burns in terms of forest cover, forage production, and wildlife utilization. The effects of the prescribed burn treatments on forest cover were evaluated with pre-burn and post-burn aerial photographs. A total of 36 transects were located in paired burned and unburned habitats. Vegetation, wildlife use, and standing crop production (production clip) data were collected at each transect. The monitoring program operated from 1998 to 2007. In general, the prescribed mitigation burns were successful. Although variable between treatment areas and years, standing crop production measurements showed consistently that forage production and, consequently, Animal Unit Months (AUMs) were greater within the burn treatment areas compared with non-burned areas. The increased cover of palatable grasses and forbs was particularly beneficial for the enhancement of elk winter range. Canopy reduction ranged from 10% to 60% within the off-site burned areas. The prescribed burns also altered species dominance and stand structure. Signs of habitat use (i.e., evidence of browsing/grazing and pellet groups) indicated that elk and mule deer continued to use the burned sites preferentially during the monitoring period.
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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 | 0.000 | 0.000 |
| 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) | 0.003 | 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".