Editorial: Community series in insights of gut microbiota: probiotics and bioactive compounds, volume II
Bibliographic record
Abstract
The last two decades have seen varying investigations into the efficacy of probiotics and bioactive 13 compounds with potential industrial and therapeutic applications. Probiotics produce a range of 14 pathogen-inhibiting compounds like bacteriocins, organic acids, and exopolysaccharides, gaining pressures, especially in the aged, is of global interest. The authors showed that gut microbiome 55 alterations had been linked with RA onset and progression and reported studies indicating that 56 specific probiotic strains incorporated into diets can alleviate its effects. Although many of these 57 studies are only recently emerging, and some of these therapeutic effects were strain-specific, it 58 was noted that these findings were promising and warrant further investigations.Non-alcoholic steatohepatitis (NASH) is still a considerable global burden, and although strategies 61 for its treatment have been researched extensively, no definite agent has been approved. Yang et In conclusion, the submissions on this Research Topic have provided a compendium of data for 95 readers to ruminate on. Undoubtedly, some findings may appear to challenge what we currently 96 know, but it is imperative that, as scientists, we approach them with an open mind and be spurred 97 to conduct more detailed studies, which would hopefully be hosted in a follow-up Research Topic.Several useful recommendations were also made and thus warrant careful observations.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.024 | 0.016 |
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".