Sensory evaluation of Cabernet franc wines in the Niagara Peninsula.
Bibliographic record
Abstract
Le but de cette etude etait de developper une methodologie sensorielle pour la caracterisation des vins de Cabernet franc des vignes typiques a moins de dix sous-appellations dans la peninsule de Niagara (Ontario, Canada). Neuf (2005) et huit (2006) vins experimentaux de Cabernet franc de peninsule de Niagara ont ete analyses pour illustrer les differences qui pourraient soutenir le systeme de sous-appellation de Niagara. Douze juges qualifies ont evalue six aromes (fruit rouge, griotte, cassis, poivre noir, paprika, et haricot vert), et trois descripteurs gustatifs (astringence, amertume et acidite) attributs sensoriels de l'effet dans la bouche comme l'intensite de couleur. L'analyse de la variance (ANOVA) des donnees sensorielles dans 2005 a montre des differences regionales pour tous les attributs sensoriels analyses. En 2006, l'ANOVA des donnees sensorielles a prouve que tous les attributs, excepte l'arome de poivre noir, etaient differents. Les vins de Lakeshore et ceux pres du fleuve Niagara ont montre un arome plus eleve de paprika et des saveurs d'haricot vert dus aux conditions croissantes de fraicheur a proximite des grandes eaux superficielles. Ces donnees indiquent qu'il y a une probabilite de differences sensorielles substantielles entre les differentes sous-appellations dans la peninsule de Niagara.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".