Effect of the new high vacuum technology on the chemical composition of maple sap and syrup
Bibliographic record
Abstract
BACKGROUND: Techniques used to produce maple syrup have considerably evolved over the last decades making them more efficient and economically profitable. However, these advances must respect composition and quality standards as well as authenticity of maple products. Recently, a new and improved high vacuum technology has been made available to producers to achieve higher sap yields. The aim of the present study was therefore to evaluate the effect of this new system on the yield of sap and on the sap and syrup chemical composition. RESULTS: Sap yield was monitored during the 2013 and 2014 seasons for high vacuum collection systems (25-28 InHg) and compared to the control systems (20 InHg). Samples of sap and syrup were also collected for chemical analysis. During the 2013 season, a sap volume of 166.19 L/tap was recorded at 25 InHg vacuum level while the control vacuum level permitted to collect 139.47 L/tap, corresponding to a yield increase of 19.2 %. The following season, a yield increase of 38.2 % was measured when control and 28 InHg vacuum levels were compared with 118.06 and 163.13 L/tap, respectively. Results on the pH, color, flavor, minerals, sugars, organic acids, total polyphenols, total nitrogen, abscisic acid and auxin (Indol-3-acetic acid) showed no major differences between high vacuum technology and the control with values remaining within ranges previously published. CONCLUSION: Results showed that a use of high vacuum systems increased sap yield and had no major impact on the quality and purity of maple sap and syrups compared with the control systems.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".