Des outils pour fiabiliser les fermentations des vins et cidres biologiques en utilisant les levures et bactéries indigènes.
Bibliographic record
Abstract
With the development of organic wines and ciders, there is a real tendency to carry out spontaneous fermentations, which involve the development of indigenous yeasts and bacteria. Indeed, these microorganisms are sometimes considered as components of the terroir that participate in the typicity of wines and ciders. However, no scientific knowledge allows us to assert such a specificity, while the lack of control of these microorganisms can lead to difficulties of fermentation, aromatic deviations or alterations. The project CASDAR Levains Bio relied on a network of laboratories, technical institutes and associations of organic producers to provide the necessary knowledge and practical solutions for carrying out indigenous fermentations with a good level of control. It has been shown that there is a wide diversity of strains of the yeast Saccharomyces cerevisiae and the lactic acid bacterium Oenococcus oeni, that strains are genetically adapted to certain products, but not to regions or production sites. Protocols have been developed to allow for the selection of strains from farms or for the production of "pieds de cuve". Some of the solutions have been successfully transferred to producers.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".