Assessment of microbial composition across the Quebec dairy farm-scape
Bibliographic record
Abstract
AIMS: The aim of this preliminary study was to evaluate the shared composition and structure of the microbiome between cow feces, milk, manure, and soil samples across dairy farms in Quebec. METHODS AND RESULTS: Analysis of the prokaryotic 16S, eukaryotic 18S, and fungal ITS rRNA genes was used to detect community structures, diversities, and spatial compositions, alongside potential contamination routes. Community structures of prokaryotic, eukaryotic, and fungal microbiomes were significantly different between niches and could be separated based on mean temperature. Bacteria and eukaryotes showed the highest diversity in fecal samples, whereas fungi were most diverse in soil. Percent of amplicon sequence variants (ASVs) shared between feces, manure, and the other matrices varied among microbial markers, with the highest values for fungi. The contamination route prediction also identified the highest probability of transmission of fungi using the ITS marker. CONCLUSIONS: Sample type had the greatest influence on microbiome structure, although temperature had an impact on microbial composition of soil and manure samples. Results elucidate interactions at the animal-environment interface, informing the need for a better understanding of fungal transfer and seasonal variability.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".