Bacterial topography of the bovine respiratory tract
Bibliographic record
Abstract
It is generally accepted that the nasopharynx is the primary source of bovine respiratory disease (BRD) pathogens for the lower respiratory tract (LRT) of cattle. Indeed, it has long been held that the nasopharynx plays a causative role in the development of BRD. However, it has been shown that there is poor agreement between the microbiota of the nasopharynx and the LRT in cattle. Conflicting results from recent studies suggest that BRD pathogens in the nasopharynx may not always significantly differ between healthy cattle and those diagnosed with BRD. These observations make sense in light of recent research in human medicine, which suggests that bacterial pathogens that cause lung infections don’t originate from the nasopharynx and that the nasopharyngeal microbiota contribute very little to composition of lung microbiota. There was surprisingly more similarity between the mouth/oropharyngeal microbiotas and the lung microbiota. In the pursuit of finding alternative strategies for controlling BRD at the feedlot such as probiotics or local antimicrobials, it is crucial to understand which upper respiratory tract (URT) microbiotas contribute the most to the LRT microbiotas so that we can modulate the correct bacterial communities. Therefore, the objective of this study is to map the bacterial microbiotas present along the entire beef cattle respiratory tract and determine the relative contributions of these communities to the microbiota of the lung.
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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 | 0.001 | 0.001 |
| 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".