Bibliographic record
Abstract
Pinot gris, Riesling, Cabernet franc, and Cabernet Sauvignon vines from a single vineyard in Virgil, Ontario were subjected to two crop levels, full crop (FC) and half crop (HC), in which crop was reduced in HC to one basal cluster per shoot at veraison. Crop level treatments were combined with three harvest dates: T0 (commercial harvest), T1 (three weeks after T0), and T2 (six weeks after T0), all with subsequent wine production. Berries, must, and wine were analyzed. Reductions in crop led to an increase in Brix, reduced yield, and cluster number in all cultivars, and increased cluster weight in Cabernet franc. Delayed harvest date also increased Brix and pH and reduced titratable acidity (TA) and berry weight. Effect of harvest date in berries carried over to musts and wines: increased pH and TA in T2 treatments was associated with reduced anthocyanins, phenols, and color intensity in red cultivars. Delayed harvest date had a greater magnitude of effect than crop reduction; thus, maintaining a full crop with a later harvest date might have a greater beneficial impact on potential wine quality than reducing crop level.
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.
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 | 0.021 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.022 | 0.015 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".