Cellulose-Based Hydrogel Capsules to Address Soil Erosion Following Wildfire
Bibliographic record
Abstract
While natural forest fires promote regrowth and restoration within an ecosystem, a major cause for concern in Canada is deforestation due to an increased prevalence of wildfires brought about by industry practices and climate change. This has become even more evident recently with the wildfires burning in remote areas of British Columbia, Alberta, Ontario, and Quebec, creating smoke-related air pollution observed in many communities throughout Canada and the United States. Entwistle, Alberta, specifically, experienced severe wildfire during late April to early May of this year (2023), leading to numerous resident evacuations while firefighters worked to contain the blaze. Our proposed grassroots initiative involves the use of biodegradable cellulose-based hydrogels to promote water retention and soil health in the fire-ravaged forested areas of Entwistle, Alberta. Cellulose is the most abundant renewable biopolymer and is utilized for the synthesis of bio-based hydrogel. It is a green and sustainable material, giving it a biocompatible advantage over petroleum-based and synthetic hydrogels that possess toxic effects. It is derived from plant material and is fully biodegradable. Soil becomes dehydrated as a result of wildfire, with decreased water content and microbe presence leading to soil erosion and deeply impacting regeneration of forests. This has negative impacts on the broader environment, as well as on humans and non-human animals. The use of hydrogels to restore and maintain soil moisture levels, pH, and microbial environments is a novel approach to better maintain forest health from a One Health perspective.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".