Predicting the date of leaf emergence for sugar maple across its native range
Bibliographic record
Abstract
A combined winter chilling and spring warming model is presented for predicting the date for the onset of foliation of sugar maple (Acer saccharum Marsh.) trees. The model is calibrated using both local data obtained in two sugar maple stands during two consecutive years with contrasting foliation dates and data obtained from the literature and chosen to span the full range of sugar maple distribution. Despite the disparity of the data used, more than 84% of the variation for the observed foliation date is explained by the model. Forty-one days separate the earliest and the latest foliation dates, and on average, the predicted date is within an interval of ±1.5 days of the observed date. Unusual events like exceptionally cool or warm springs are also well represented by the model. The counts for chilling days and degree-days are both started on December 1, but choosing any other date between November 1 and April 1 would yield nearly as good a fit to the foliation data. Temperature thresholds for chilling days and degree-days are both set at 10°C. Although this temperature gives the best fit to the foliation data, any temperature down to about 3°C would give good results as long as both threshold temperatures are the same.
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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 | 0.000 |
| 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) | 0.001 | 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".