Perceptions of U.S. and Canadian maple syrup producers toward climate change, its impacts, and potential adaptation measures
Bibliographic record
Abstract
The production of maple syrup is an important cultural and economic activity directly related to the climate of northeastern North America. As a result, there are signs that climate change could have negative impacts on maple syrup production in the next decades, particularly for regions located at the southern margins of the sugar maple (Acer saccharum Marsh.) range. The purpose of this survey study is to present the beliefs and opinions of maple syrup producers of Canada (N = 241) and the U.S. (N = 113) on climate change in general, its impacts on sugar maple health and maple syrup production, and potential adaptation measures. Using conditional inference classification trees, we examined how the socio-economic profile of respondents and the geographic location and size of respondents' sugar bushes shaped the responses of survey participants. While a majority (75%) of respondents are confident that the average temperature on Earth is increasing, less than half (46%) believe that climate change will have negative impacts on maple syrup yield in the next 30 years. Political view was a significant predictor of these results, with respondents at the right right and center-right of the political spectrum being less likely to believe in climate change and less likely to anticipate negative effects of climate change on maple syrup production. In addition, 77% of the participants indicated an interest in adopting adaptation strategies if those could increase maple syrup production. This interest was greater for respondents using vacuum tubing for sap collection than other collection methods. However, for many respondents (particularly in Canada), lack of information was identified as a constraint limiting adaptation to climate change.
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.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".