One Health, One Forest: Harnessing Reclaimed Wood as a Sustainable Solution
Bibliographic record
Abstract
Deforestation is a multifaceted and wicked problem characterized by its complexity and resistance to straightforward solutions. The issue is driven by human activities and has severe ecological, socio-economic, and climatic consequences. Between 1990 and 2015, approximately 129 million hectares of forest were lost globally, a trend contributing to biodiversity loss, increased carbon dioxide emissions, and climate change. In Canada, deforestation due to logging significantly impacts the boreal forests, with consequences such as habitat fragmentation affecting species like the threatened boreal caribou. The Canadian logging industry aims to provide essential raw materials while fostering economic growth and employment, supplying critical resources for sawmills, planing mills, shingle mills, and pulp and paper industries. Despite economic benefits, logging, particularly clearcutting, disrupts natural forest regeneration, soil composition, and water cycles, leading to long-term ecological consequences. The One Health approach, integrating human, non-human animal, and environmental health, is proposed to address this issue sustainably. Actions like those by Forests Ontario and Evergreen focus on reforestation and urban greening, while companies like Consolidated Pallet Co. promote wood recycling. This action plan showcases the potential for community-driven solutions to reduce environmental footprints, enhance sustainability, and foster economic and social well-being.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".