Climate Change Impacting the Freeze Thaw Cycle of Sugar Maple Tree Sap Productivity
Bibliographic record
Abstract
The Canadian maple syrup industry accounts for over 600 million dollars of Canada’s economy each year and provides jobs to thousands of Canadians (Agriculture and Agri-Food Canada, 2025). This product synonymous to the Canadian identity is under threat as recent years have seen some of the lowest maple syrup production in history (Agriculture and Agri-Food Canada, 2025). This drop off is scientifically believed to be as result of climate change and other anthropocentric impacts on the environment which have led to a decline in the health of sugar maple trees (acer saccharum) (Boakye et al., 2023). These trees have seen a reduction in their overall health and growth rate as well as changes to their environments which are becoming more extreme (Boakye et al., 2023). This paper uses dendrochronology and sap collection reports to see if there has been an observable change in tree growth rate, health, and sap production in tandem with climate monitoring data to study changes in the environment. Studying these variables not only grants a view into the changes in health and success of the trees with climate change, but also see if there has been an overall shift in what could be described as the “optimal” geographic area for these trees to grow and the possible economic impacts of that change (Rapp et al., 2019).
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".