Fecal microbiota of diarrheic calves: Before, during, and after recovering from disease
Bibliographic record
Abstract
BACKGROUND: It is unknown if gastrointestinal dysbiosis in diarrheic calves causes disease or is a consequence of the disease. OBJECTIVES: Describe the fecal microbiota of calves before, during, and after recovering from diarrhea. ANIMALS: Fifteen female Holstein calves of 0 to 21 days old from a single farm. Seven calves remained healthy throughout the study, and 8 developed diarrhea on Day 14. METHODS: Longitudinal cohort study. Microbiota composition was characterized by amplifying the V4 region of the 16S rRNA gene. RESULTS: Diversity (Shannon index) increased with age in healthy and diarrheic calves from Day 3 to 21, but diarrheic calves had a lower diversity on the day diarrhea was first observed (Day 14). By Day 21, diversity increased in calves that recovered from diarrhea and was not significantly different from that of their healthy counterparts (P > .05). Weighted UniFrac distance showed significant differences in the fecal microbiota between diarrheic and healthy calves at Day 14 of age (PERMANOVA, P < .05), but not before or after diarrhea (PERMANOVA, P > .05). Lactobacillus, Clostridium Sensu Stricto 1, and Collinsella were differentially abundant on Day 10 in calves that developed diarrhea on Day 14 (P < .05). CONCLUSION AND CLINICAL IMPORTANCE: The fecal microbiota of healthy and diarrheic calves evolved similarly during the first 10 days of age but differed significantly on the day of onset of diarrhea. Enriching Lactobacillus, Clostridium Sensu Stricto 1, and Collinsella before diarrhea onset could have been contributed to the development of diarrhea.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".