Non-Timber Forest Products, Maple Syrup and Climate Change
Bibliographic record
Abstract
Non-timber forest products (NTFP), including maple syrup, are an important source of income in rural and remote spaces. NTFPs also contribute to other aspects of rural wellbeing including the provision of environmental services and opportunities for the development and maintenance of social capital and aesthetic/spiritual values. NFTPs are thought to be threatened by climate change, yet little research has been undertaken to assess the potential impacts and adaptive capacity of affected Canadian rural spaces. Maple syrup is one of Canada's most important NTFPs and an important resource in central Canada and Atlantic rural spaces. However, virtually no research has assessed the value of maple syrup as an NTFP, or the potential impact of climate change. This paper, which is part of a larger on-going study, will report on survey work that assessed perceptions of institutional contexts, climatic variability, climate change risk, and resiliency within the maple syrup industry. The results will be of interest to decision-makers in many areas including the maple syrup industry, Canadian rural policy and climate change policy. Drawing from the survey work and broader study findings, the paper identifies existing capabilities and challenges for dealing with climate change and outlines potential opportunities to increase the adaptive capacity of the maple syrup industry and rural spaces. Keywords: maple syrup, climate change, policy, adaptation, Canada, Ontario
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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 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".