Bibliographic record
Abstract
Lactic acid bacteria relate in many ways to the well-being and the economic activity of humans. Lactic acid bacteria are recognised as human pathogens but also are beneficial members of commensal microbial communities in humans and animals, and are used as probiotics. Lactic acid bacteria impact food quality and safety both as beneficial fermentation microbes and as spoilage organisms. Owing to their multi-faceted relationship to humans, the carbohydrate metabolism of lactic acid bacteria has been subject to research for more than a century. The aim of this review is to link the wealth of knowledge on lactic metabolism to the currently available genomic information on lactic acid bacteria. Homofermentative and heterofermentative lactic acid bacteria take a substantially different approach to energy generation, have different substrate preferences and thus co-exist in multiple ecological niches. Many lactic acid bacteria maintain electron transfer chains to use oxygen, nitrate or iron as terminal electron acceptors under suitable conditions. The metabolism of organic acids and diols contributes to acid resistance and support stationary-phase survival. The ecology of lactic acid bacteria also shapes metabolic preferences and several metabolic traits differentiate insect associated, vertebrate-host adapted, free living and domesticated lactic acid bacteria. Knowledge of metabolic preferences that relate to phylogeny or the adaptation to different ecological niches facilitates selection of starter cultures for conventional and novel food fermentations.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".