Identifying Oral Bacterial Microflora Associated with Canine Dental Plaque A Study of 53 Canines
Bibliographic record
Abstract
A total of 1594 dogs were screened and 53 dogs were found positive for dental affections. Recorded prevalence was 27 (50.94%) in Spitz cross, 5 (9.43%) in Doberman, 5 (9.43%) in Labrador, 8 (15.09%) in German shepherd, 1 (1.8%) in Great Dane, 1 (1.8%) in Saint Bernard and 6 (11.3%) in non-descript. Bacteria were isolated from the dental plaques and identified by Gram's staining. Among 27 samples, 8 bacterial genera were identified and those were of Neisseria (11.11%), Staphylococcus (29.62%), Bacillus (22.22%), Escherichia (14.81%), Moraxella (11.11%), Pseudomonas (3.73%), Streptococcus (11.11%) and Corynebacterium (3.73%).
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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) | 0.001 | 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 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".