Prediction of Maple Syrup Quality from Maple Sap with a Plasmonic Tongue and Ordinal Mixed-Effects Modeling
Bibliographic record
Abstract
A gold nanoparticle (Au NP)-based plasmonic tongue is shown to correlate well with the emergence of flavor defects in the late season harvest of maple syrup, validated with a representative sampling of 29 304 maple syrups of different grades. The daily average temperatures, pH, transmittance, °Brix, and total and individual amino acid concentrations provided evidence that the plasmonic tongue responds to amino acid concentrations, which is then correlated to an off-flavor index. The amino acid to sugar ratio decreased significantly in syrup compared to sap, a result of their consumption in the Maillard reaction during the boiling process. An ordinal mixed-effect model was shown to accurately predict the amino acid concentrations and the most likely grading class of maple syrup from the plasmonic tongue’s response. Taken together, the plasmonic tongue with the mathematical model could serve as a predictor of the output quality of maple syrup from maple sap at the production site.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".