High-Throughput OEnomics: Shotgun Polyphenomics of Wines
Bibliographic record
Abstract
Red wine is a complex mixture of organic compounds including polyphenols and the use of ultra high performance liquid chromatography-electrospray ionization-quadrupole-time-of-flight-mass spectrometry (UHPLC-ESI-Q-TOF-MS) is a promising technique to better understand its quality and authenticity. To optimize the characterization of red wine, we developed an original and fast method that represents the first shotgun polyphenomics analysis of wine. We show that our new method yields significantly more information than previous fast methods such as direct injection-ESI-MS of wine, with the identification of 103 compounds in 2 min. As a first application, we show that the use of a specific selected ion ratio demonstrates significant differences between Pinot Noir, Merlot, and Syrah wine spectra in a preliminary study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.008 | 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 teacher head, 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".