Soil Organic Matter: A Sustainability Indicator for Wildfire Control and Bioenergy Production in the Urban/Forest Interface
Bibliographic record
Abstract
Many rural communities in British Columbia (western Canada) are increasingly at risk from wildfire as temperatures rise and droughts become more frequent. In addition, these communities are also faced with rising fuel costs and a growing demand for heat as their populations increase. The fact that these communities are surrounded by forests presents an opportunity to combine community wildfire risk abatement with bioenergy development. We show how the ecological model FORECAST was linked with GIS and economic models to create a freely available online tool (FIRST Heat) to help other communities make their own screening‐level ecological assessments of combining wildfire risk control with district heating systems. The tool incorporates an ecological sustainability index based on the relative change in soil organic matter (SOM) after 50 yr of management compared with initial levels. Two thresholds were defined: 10% SOM lost (warning level) and 20% SOM lost (critical level). The tool was able to adequately capture the influences of ecological zone, stand age, site quality, and intensity of forest management on SOM losses. Stands in the sub‐boreal and arid interior were significantly more exposed to SOM losses than in other ecological zones, as well as soils in old‐growth forests. Stands in poor sites were significantly more sensitive to forest management than young and fertile sites. All things considered, our results show the suitability of incorporating ecological models and SOM thresholds in user‐friendly decision‐support tools to successfully transfer scientific knowledge on forest soils to local stakeholders and decision makers.
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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 | 0.002 | 0.002 |
| 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) | 0.002 | 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".