Bibliographic record
Abstract
1. Introduction, 589 2. Citrate fermentation, 589 3. Metabolism of carbohydrates, 590 3.1 Metabolism of mono‐ and disaccharides, 590 3.2 Metabolism of polysaccharides, 591 3.3 Metabolism of polyols, 591 4. Catabolism of aldehydes, 592 5. Hydrolysis of glycosides, 592 6. Degradation of phenolic acids, 593 7. Synthesis and hydrolysis of esters, 593 8. Lipolysis, 593 9. Proteolysis and peptidolysis, 593 10. Amino acid catabolism, 594 11. Sensory impact, 595 12. Health implications, 595 12.1 Formation of amines, 595 12.2 Formation of ethyl carbamate precursors, 595 12.3 Formation of glyoxal and methylglyoxal, 596 13. Conclusions, 596 14. References, 596 Malolactic fermentation (MLF) in wine is a secondary fermentation that usually occurs at the end of alcoholic fermentation by yeasts, although it sometimes occurs earlier. It is practically a biological process of wine deacidification in which the dicarboxylic L‐malic acid (malate) is converted to the monocarboxylic L‐lactic acid (lactate) and carbon dioxide (Davis et al. 1985). Deacidification is particularly desirable for high‐acid wine produced in cool‐climate regions, such as New Zealand and Canada. This process is normally carried out by lactic acid bacteria (LAB) isolated from wine, including Oenococcus oeni (formerly Leuconostoc oenos; Dicks et al. 1995), Lactobacillus spp. and Pediococcus spp. (Wibowo et al. 1985). Various technologies, such as bioreactors with high‐density cells and immobilized cells or enzymes, have been developed to facilitate wine deacidification (Maicas 2001). Oenococcus oeni is the preferred species used to conduct MLF due to its acid tolerance and flavour profile produced. In addition to its occurrence in wine, MLF occurs in other fermented beverages, such as cider (Carr 1987; Jarvis et al. 1995).
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".