Ten years of VINQUEST: first findings
Bibliographic record
Abstract
Apple scab, caused by Venturia inaequalis, is a major disease worldwide. To control this disease, up to 20-25 fungicide applications may be needed depending on the year and the production system. Cultivation of scab-resistant apple cultivars would reduce the chemical footprint of apple production. Breeding for durable scab resistance is an objective of most apple breeding programmes. One way to achieve durable resistance is to pyramid multiple apple scab resistance genes in a cultivar. Currently, more than 18 different apple scab resistance genes have been reported. Molecular markers are available for most of these and can be used to select seedlings carrying multiple resistance genes. Apple scab isolates are virulent to specific apple scab resistance genes. Choice of optimal resistance genes for use in a breeding program, should therefore take regional presence of these apple scab isolates into consideration. In order to learn more about the geographical distribution of apple scab isolates, the VINQUEST project (www.vinquest.ch) was initiated in 2009. Ten years after the launch of this initiative, 24 partners from 14 countries are regularly contributing data. Results obtained from the data collected during the first 10 years provide the first insights into which apple scab resistance genes may hold most promise for breeding cultivars with durable resistance.
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.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".