Impacts of Climate Change on the Timing of the Production Season of Maple Syrup in Eastern Canada
Bibliographic record
Abstract
Maple syrup production is an important economic activity in north-eastern North-America. The beginning and length of the production season is linked to daily variation in temperature. There are increasing concerns about the potential impact of climatic change on this industry. Here, we used weekly data of syrup yield for the 1999-2011 period from 121 maple stands in 11 regions of Québec (Canada) to predict how the period of production may be impacted by climate warming. The date at which the production begins is highly variable between years with an average range of 36 days among the regions. However, the average start date for a given region, which ranged from Julian day 65 to 83, was highly predictable (r2 = 0.88) using the average temperature from January to April (TJ-A). A logistic model predicting the weekly presence or absence of production was also developed. Using the inputs of 77 future climate scenarios issued from global models, projections of future production timing were made based on average TJ-A and on the logistic model. The projections of both approaches were in very good agreement and suggest that the sap season will be displaced to occur 15-19 days earlier on average in the 2080-2100 period. The data also show that the displacement in time will not be accompanied by a greater between years variability in the beginning of the season. However, in the southern part of Québec, very short periods of syrup production due to unfavourable conditions in the spring will occur more frequently in the future although their absolute frequencies will remain low.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".