Representing tissue mass and morphology in mechanistic models of digestive function in ruminants
Bibliographic record
Abstract
Representing changes in morphological and histological characteristics of epithelial tissue in the rumen and intestine and to evaluate their implications for absorption and tissue mass in models of digestive function requires a quantitative approach. The aim of the present study was to quantify tissue mass (M) and absorptive area (AA) from parameters that are easily derived from morphological inspection and histology of tissue biopsies, and to compare this representation with approaches in current model of digestive function. Relatively small changes of 5% in some morphological and histological characteristics were calculated to affect absorptive area (AA) and epithelial tissue mass (M) strongly in the rumen and the intestine of cattle. The cumulative effect of changes in volume, height and width of primary protrusions and of secondary protrusions of the rumen wall was 18% for rumen mucosal AA and 29% for rumen serosal AA. It was 24% for intestinal mucosal AA. The cumulative effect on rumen and intestinal M was 20% and 22%, respectively. The simulations indicate that allometric functions that relate volume or weight to AA and M require exponentiation with an exponent higher than those based on geometric shape or volume.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".