Bibliographic record
Abstract
Bovine digital dermatitis (DD) is a skin disease affecting cattle worldwide. As a significant contributor to infectious lameness, DD is increasingly an economic and animal welfare concern for dairy and beef producers. DD is a polymicrobial disease, with many different types of anaerobic bacteria strongly associated with lesions. No causative agents or etiopathogenesis mechanisms of DD are yet accepted due to the multiple different species present and microbiota variation among individual lesions. The involvement of Treponema in lesion development is generally accepted, whereas what combination of other anaerobic bacteria are involved is currently debated. Insufficient and incomplete identification and characterization of these additional species are a limiting factor in DD pathogenesis research. This thesis aimed to fill the gaps in knowledge of these additional anaerobes throughout DD lesions, while providing the first comprehensive description of DD microbiota in feedlot beef cattle. Through high throughput sequencing and culturing of DD tissue biopsies, we identified Treponema, Mycoplasma, Fusobacterium spp., Porphyromas levii, and Bacteroides pyogenes as potential DD pathogens, and used a multiplex qPCR for absolute quantification and reproducible characterization of species population dynamics. We identified T. medium, P. levii, and T. phagedenis as a group strongly associated with early lesion stages, thus potentially being involved in lesion formation. Through a meta-analysis of all publicly available DD metagenomic studies, we identify Treponema, Mycoplasma, Fusobacterium, and Porphyromonas as the primary DD-associated microbiota. We recommend focusing future DD research efforts on culturing and characterizing species of these groups in an effort to establish etiopathogenesis mechanisms of DD.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".