TAMM review: On the importance of tap and tree characteristics in maple sugaring
Bibliographic record
Abstract
Maple sugaring mainly uses sugar and red maples (Acer saccharum and Acer rubrum) by tapping them for sap in the leafless-state across large portions of their ranges. How much sap exudes from a tap hole and how sweet this sap is, can vary substantially. Year-to-year variation in sap yield and sugar content can be primarily traced to differences in meteorological conditions that drive sap runs. Yet, how much of the total variation in sap yield and sugar content is linked to the year, site, species, tree, or tap has not been investigated systematically. Here, we reviewed the literature and also compiled a dataset of sap yield and sugar content from gravity taps on 324 red and sugar maples. The compiled data originates from multiple studies at ten sites across a large proportion of the ranges of sugar and red maple and stretches over eleven years. Using about 15 000 data points on sap yield and sap sugar content, we analysed the importance of tap and tree characteristics, such as height of the tap hole on the stem or diameter at breast height. We also review previous research on the importance of tap and tree characteristics in maple sugaring. Moreover, we partition variability in the data to attribute it to species, site, tree, year, and tap characteristics. Our results indicate that species, site and tree characteristics are the three largest sources of variability with regards to sap yield and the sap’s sucrose concentration. However, differences between years and tap characteristics, which were found to be comparatively minor sources of variability in sap yield and the sap’s sucrose concentration, have attracted far more attention in the past. We advocate for the continuation and expansion of systematic measurements of sap characteristics across a network of sites to further improve our understanding of maple sugaring. Such an understanding will be instrumental to prepare maple sugaring operations against the imminent effects of the climate and biodiversity crises and ensure their sustainability to perpetuate this traditional activity.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".