Reassessing the schedule of the sugar season in maple under climate warming
Bibliographic record
Abstract
Daily temperature fluctuations trigger physical and metabolic processes in the xylem, affecting the timing and yield of maple sap production. This study evaluates sap production dynamics, examining the effects of mean monthly temperatures and freeze-thaw cycles before and during the sugar season. We developed a predictive model estimating sap phenology, i.e. the timings of sap season and their climatic drivers, under future warming scenarios in Quebec, Canada. We collected air temperatures and daily sap production at four study sites in 2022 and 2023 using rain gauges for simulating a gravity collection of sap. We estimated sap phenology using a neural network model based on average monthly temperatures. The length of the sugar season was consistent across and within sites, with the highly productive days showing similar occurrence across sites. Sap yields ranged from 9.28 to 23.8 liters in 2022 and 3.8 to 13.6 liters in 2023. Freeze-thaw events occurred on 64% of the days when sap was exuded. Our neural network model predicted that a 2°C increase in mean monthly temperatures would advance the sugar season start by 17 days and end by 13 days. Any mismatch between tapping and favorable weather conditions can significantly reduce sap production. With climate change, producers will be forced to progressively readjust the schedule of their field activities and tapping to match the shifting sugar season.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".