The effects of ambient temperature exposure on feline fecal metabolome
Bibliographic record
Abstract
Introduction The fecal metabolome provides insight into overall gastrointestinal and microbial health. Methods for fecal sample storage in metabolomics research vary, however, making comparisons within current literature difficult. This study investigated the effect of ambient temperature exposure on microbial-derived metabolites of feline fecal samples. Methods Fecal samples were collected from 11 healthy cats from a local boarding facility. Samples were manually homogenized and aliquoted. The first aliquot was frozen at -80°C within 1 hour of defecation, and remaining samples were exposed to ambient temperature for 2, 4, 6, 8, 12, and 24 h prior to freezing at -80°C. Fecal metabolites were quantified using 1H NMR spectroscopy. Fifty metabolites were grouped into six categories (27 amino acids, 8 fatty acids, 5 sugars, 3 alcohols, 2 nitrogenous bases, 5 miscellaneous). Results Concentrations of 20 out of 50 metabolites significantly differed due to ambient temperature exposure (7 amino acids, 6 fatty acids, 2 alcohols, 1 nitrogenous base, 4 miscellaneous). The earliest detected changes occurred 6 h post-defecation for cadaverine and fumaric acid. Discussion This study shows ambient temperature exposure alters the composition of the feline fecal metabolome, but short-term (up to 4 h) exposure prior to storage in the freezer seems to be acceptable.
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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 | 0.000 |
| 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 | 0.000 |
| 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".