Bibliographic record
Abstract
The intestinal microbiota is a diverse and dynamic ecosystem,1 which has developed a mutualistic relationship with its host and plays a crucial role in the development of the host's innate and adaptive immune responses.2 This ecosystem serves the host by protecting against pathogens, harvesting otherwise inaccessible nutrients, aiding in neutralisation of drugs and carcinogens, and affecting the metabolism of lipids.3 Gut bacteria modulate intestinal motility, barrier function and visceral perception.4 An interaction between the intestinal microbiota and the central nervous system (CNS) may seem difficult to conceive at first sight, but clinicians are well aware of the benefit of oral antibiotics and laxatives in the treatment of hepatic encephalopathy.5 Data accumulated from animal studies indicate that there is central sensing of gastrointestinal infections. For example, acute infection with Campylobacter jejuni results in anxiety-like behaviour and rapid activation of vagal pathways prior to onset of immune responses,6 while chronic Helicobacter pylori infection in mice leads to abnormal feeding behaviour and upregulation of tumour necrosis factor α (TNFα) in the median eminence of the hypothalamus.7 Rapid and sustained gut–brain communication may confer a significant advantage to the host, as central activation in response to changes in commensals or pathogens would allow better control of gut function and immunity. How does the intestinal microbiota …
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.016 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".