One Health Approach Utilizing Mycelium to Prevent Wildfires in Southeast Ontario
Bibliographic record
Abstract
The prevalence and intensity of wildfires have increased dramatically in Ontario, Canada since 2022. This has been exacerbated by climate change, clearcut logging, and poorly extinguished campfires. While previous interventions have targeted the downstream effects of wildfires such as deforestation, there have been no interventions that utilize a One Health approach to equally consider the health of humans, non-human animals, and the environment. This paper proposes a novel and cost-effective initiative utilizing cultivated mycelium from degraded slash piles, harvested and transformed into an organic fire-retardant spray for application on nearby trees. The proposed initiative aims to reduce the risk of wildfire ignition from at-risk trees in Lyndhurst, Ontario to protect the lives of humans and non-human animals as well as the integrity of properties and wildlife habitats, simultaneously contributing to the restoration of forest health as a crucial carbon sink. This may mitigate the effects of climate change and improve air quality, acting as a protective measure for human, non-human animal, and environmental health.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".