Bibliographic record
Abstract
In the past forty-five years, the field of probiotics has grown from a handful of laboratory studies and clinical ideas into a legitimate research and translational entity conferring multiple benefits to humans around the world. This has been founded upon three principles: (i) the need for alterna-tives to drugs that either have sub-optimal efficacy or severe adverse effects; (ii) a growing interest in natural products and microbes, in particular cata-lyzed by studies showing the extent of microbes within humans and on our planet; and (iii) evidence on the genetics and metabolic properties of probi-otic strains, and clinical studies showing their effectiveness. While some man-ufacturers have sadly taken advantage of the market growth to sell supple-ments and foods they term probiotic, without the necessary human study evidence, there are more and more companies basing their formulations on science. Adherence to the definition of what constitutes a probiotic, conclu-sions based on tested products not generalizations of the whole field, and applications emanating from microbiome research identifying new strains that provide benefits, will make the next forty-five years significantly changed approaches to health management. Exciting applications will emerge for car-diovascular, urogenital, respiratory, brain, digestive and skin health, detoxifi-cation, as well as usage across the world's ecosystems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".