One Health Approach Utilizing Mycelium to Prevent Wildfires in Southeast Ontario
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.000 | 0.001 |
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 it