Use of ampelographic methods in the identification of Nova Scotian grape (Vitis spp) cultivars
Bibliographic record
Abstract
In recent years the evaluation of suitable grape (Vitis spp.) cultivars for the Nova Scotia grape growing and wine industries has become increasingly important. Despite this, little material exists documenting the morphological characteristics of grape cultivars grown in Nova Scotia. This lack of material could make identifying unknown cultivars in a vineyard problematic. The objectives of this study were to describe grape cultivars based on several ampelographic characteristics proposed by Pierre Galet (1979), and to evaluate these characteristics to determine their usefulness in identification. Twenty-nine cultivars that are currently grown in Nova Scotia were described based on morphological characteristics such as leaf colour, hair type, tendril placement and tooth size and shape. Leaf measurements, such as vein length ratios, leaf size and sinus depth, were also used to describe cultivars. Results demonstrated which characteristics were useful in identification and which were not. For instance, indument, or hairiness, was useful as it allowed all cultivars to be divided into numerous small groups, and in some cases narrowed down the identities of single cultivars, such as Einset and KW96-1. Other characteristics, such as tendril placement, were less useful as all sampled cultivars showed an identical pattern. In many cases vein length ratio measurements were also of little use, as these measurements tended to differ little between cultivars. The results of this study provided a preliminary means of cultivar identification and an approach to identification that did not previously exist for Nova Scotian viticulture. However, future work is required to test this proposed method of identification. Additional cultivars and characteristics should also be included in future work.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".