Influence of foliage management on lyra for «high quality» wines production for Cabernet-Sauvignon variety: enological aspects (I note)
Bibliographic record
Abstract
Cabernet-Sauvignon is an important red berry cultivar, which provides in Latium good quality results even if grown using training systems and planting models which are notably different among themselves . To give a concrete contribution to the qualitative improvement of « Cabernet-Sauvignon », considering other viticultural research exposed in other works, we thought it was opportune to deepen the repercussion of foliage management. Among many models of training systems that we have taken into consideration over years of experimentation, the LYRA order 300 cm x 50 cm has given the better results regarding oenochemical, sensorial and economical quality of wines. For this reason we have considered the implications of different foliage management systems on this model, drawing the following indications: a) The training system which has shown the best results was LYRA order 300 cm x 50 cm for «Cabernet-Sauvignon» variety, even with different foliage management. b) The best analytical results are achieved by LYRA « Managed Volume » foliage, especially concerning the chromatic component. c) The sensorial analysis confirms the excellence of this treatment. d) The better «economic quality» is achieved to LYRA « Managed Volume » foliage; in fact the consumers have attributed the highest «intrinsic value» to the corresponding wine. e) In conclu,es must be checked in the next vintages.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".