Modeling grape quality by multivariate analysis of viticulture practices, soil and climate
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
Aims. The present study aims to model grape quality criteria by combining a large number of viticultural practices and soil and climate variables related to the main determinants.Methods and results. A database has been developed using the Chenin Blanc grape variety in a Protected Designation of Origin. A statistical model, namely a Partial Least Squares (PLS) regression, has been determined for each grape quality criterion (sugar content, total acidity, malic acid, tartaric acid, available nitrogen, pH and bunch rot). This statistical analysis identifies the main viticultural practices and soil and climate variables related to the grape quality at harvest. The results highlight relationships between the length of vine pruning and pH and malic acid but even more significant relationships with tartaric acid, available nitrogen and bunch rot.Conclusion. The models point out the most relevant viticultural practices and soil and climate variables for the explanation of each grape quality criterion studied.Significance and impact of the study. The results provide a better understanding of the major variables that influence grape quality.
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 | 0.001 |
| 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