Bibliographic record
Abstract
Failure of pregnancy in dogs and cats is approached as a diagnostic investigation of an individual animal. It begins with a preliminarydiscussion on zoonotic disease and expectations (seldom is the case) of evaluation. General approach from a pathologypoint of view is to identify potential infectious causes of pregnancy failure and to correlate these observations with lesions. Potentialinfectious agents include eubacteria, fungi, viruses and protists. When infectious causes have been ruled out, the focus isthen on noninfectious causes and particularly those with lesions. Maternal evaluation, including endometrial biopsy after uterineinvolution, is part of the investigation. Special effort should be made to collect umbilical arteries and lungs of fetus, and fetalmembranes around the marginal hematoma. Failure of pregnancy due to noninfectious causes without lesions contributes to alarge percentage of cases, suggesting maternal, paternal, molecular, nutritional, or toxic causes.
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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 | 0.001 | 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".