Investigation into the Formation of Guaiacol Conjugates in Berries and Leaves of Grapevine Vitis vinifera L. Cv. Cabernet Sauvignon Using Stable Isotope Tracers Combined with HPLC-MS and MS/MS Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Fermentation of grapes that had been exposed to bushfire smoke can potentially yield unpalatable, smoke-affected wine. Guaiacol and its glucoconjugate were previously found in smoke-affected grapes at an elevated concentration. To find and identify further guaiacol conjugates in smoke-affected grapes, a stable isotope feeding experiment combined with extensive HPLC-MS and MS/MS investigations was carried out. Leaves and berries of a potted grapevine were placed in contact with an aqueous mixture of d(0)- and d(3)-guaiacol for 1-2 days and collected 5 weeks later. Screening for potential guaiacol conjugates in the leaves and berries was facilitated by monitoring the unique mass spectrometric signature of an isotopic doublet separated by 3 Da. Seven different conjugates were detected in leaves and berries and were tentatively identified as mono- and diglycosides of guaiacol. Quantitative analysis demonstrated that the guaiacol conjugates were translocated between leaves and berries to a very limited extent and were also present as low-level natural compounds of untreated leaves and berries. The same guaiacol conjugates were also found at a considerably elevated concentration in leaves and berries obtained from grapevines exposed to bushfire smoke.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 it