Can Molecular Ampelography Identify a Grapevine Variety in the Absence of Any Ampelographic Inputs?
Bibliographic record
Abstract
Is it possible to identify a grapevine variety without looking at the phenotype? Could someone reach a correct varietal identification solely based on molecular inputs? How trustful could the empirical names the grapevine growers use for the autochthonous grapevine varieties they cultivated for many decades? The current manuscript explores these questions and provides evidence supporting the concept that molecular ampelography—in terms of SSR data—could lead to an accurate varietal identification, while also sets the prerequisites for this to occur: i) a large number of samples collected from diverse cultivation areas should be analyzed, ii) multiple samples of the same variety should be included in the analysis, and iii) only local grapevine material should be considered and used in the analysis. Inclusion of reference samples (that have been properly described in ampelographic terms and are maintained in reference collections) increases the confidence of the outcome.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".