Generating bowel movements that facilitate nutrient absorption
Bibliographic record
Abstract
When nutrients enter the intestine from the stomach, the movements of the intestine caused by contractions of the intestinal musculature (“intestinal motility”) have to fulfill the function of mixing the content with digestive enzymes and optimizing absorption by exposing all content to the lining of the gut. Intestinal motility is performed by smooth muscle cells and orchestrated by intestinal pacemaker cells and by a nervous system that is unique to the intestine. Movements to promote mixing are called “segmentation”, and movements that propel content along are called “peristalsis”. Peristalsis and segmentation in the human intestine are rhythmic and are governed by pacemaker cells called “interstitial cells of Cajal” or “ICC”. With peristalsis, a network of pacemaker cells generates a wave of electrical activity that propagates into the musculature and determines the rhythm and propagation of contraction, similar to the pacemaker system of the heart. A recent discovery published in Nature Communications revealed that the segmentation motor pattern is initiated by certain nutrients that activate a second network of pacemaker cells. This second pacemaker activity is an electrical rhythmic signal that interacts with the first pacemaker activity, modifying it in such a way that the musculature responds with a completely different motility pattern; it changes the nature of the contractions from peristalsis to non-propulsive segmentation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".