16S Microbiome Assessment in Pneumonic Calves from a farm in BC Canada
Bibliographic record
Abstract
The primary goal of the study was to determine differences between the microbiomes of healthy (HLT), pneumonic (POS), and previously pneumonic (PRV) calves from a single dairy farm dealing with a high prevalence of Mycoplasmopsis bovis. 16S rRNA sequencing was completed by Norgen Biotek Corp. on the Illumina MiSeq platform. All fastq sequences generated are accessible on NCBI's Sequence Read Archive repository under the BioProject accession number PRJNA1389668. Amplicon sequence variants (ASVs) were constructed using DADA2 in QIIME2. Taxonomic classification was completed using the SILVA v138 reference database. Subsequent analyses were completed in R Statistics. This dataset includes the sample metadata (Supplementary Table 1), the differential abundance results from the ANCOM-BC2 analysis using the ANCOMBC v1.2.8 R package (Supplementary Table 2), and the taxa-taxa correlation results from the SECOM analysis which was also implemented in the ANCOMBC R package (Supplementary Table 3). Our study focused on Mycoplasmopsis bovis (M. bovis) abundance in pneumonic calves. Briefly, we found that M. bovis was more associated with the loss of commensal taxa that were enriched in healthy animals, rather than an enrichment of M. bovis in pneumonia positive animals. ANCOM-BC2 data displays the log2 fold change, p-value, and BH-adjusted q-value as well as other differential abundance metrics for each comparison. For clinical group comparisons, healthy animals were the reference. SECOM data outlines the taxa-taxa abundance correlations for each taxa among the dataset. Both nonlinear (distance) and linear (Pearson) correlation was calculated and the associated BH-adjusted p-value.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".