Bibliographic record
Abstract
Determining pregnancy failures in horses and camelids is approached as a diagnostic investigation of an individual animal. Itbegins with the preliminary discussion around expectations of the investigation and apparently, diagnostic success rate is highin horses. However, diagnostic success in camelids is low. General approach from a pathology point of view is to identify severalof noninfectious fetal and fetal membranes’ lesions and then determine the potential infectious causes; agents include viruses,eubacteria, protozoa, and fungi. Bacterial and fungal infections are mostly ascending infections whereas viral and protozoal aresystemic infections. When an infectious cause is excluded, the focus is directed on potential noninfectious causes and particularlythose with detectible lesions. Maternal evaluation, specifically, via endometrial biopsy and examination of fetus and fetal membranes(including umbilical cord) in horses, normally provide an explanation for noninfectious failure of pregnancy in severalcircumstances. Special attention should be given to fetal thyroid gland, tracheal contents, and musculoskeletal system, particularly,the medulla of long bones. In horses, noninfectious failure of pregnancy with no lesions (idiopathic abortion) is observed in asmall percentage of cases. In camelids, it is a common ‘diagnosis.’
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".