The Pregnancy Diagnosis in Buffalo Species: Laboratory Methods
Bibliographic record
Abstract
Pregnancy diagnosis plays an important role in the reproduction management of ruminants since embryonic mortality has a substantial impact on the fertility of a herd. Most of the embryonic losses occur during the first days after fertilization and during the process of implantation. So it is very important to discriminate, with an early pregnancy diagnosis, non-pregnant from pregnant animals. Hormone analysis to detect pregnancy may be utilize as a more simple technique as an alternative of rectal palpation or ultrasound. In the last years, a large polymorphic family of placenta-expressed proteins has been discovered in ruminant species and used for pregnancy diagnosis. Members of this family are named pregnancy-associated glycoproteins (PAG), being synthesized in the mono-and binucleate cells of the ruminant’s trophectoderm. Part of them is released in the maternal blood circulation where they can be assayed by different laboratory techniques. Due to large variety of expressed molecules and to large variations in the post-translational processing of the PAG, different immuno-systems present different ability to quantify the PAG released in blood. The assay of PAG can also bring very interesting information for researchers working in programs focused on the study of embryonic and fetal mortalities, as well as on embryo biotechnology, animal nutrition or infections diseases resulting in pathologies affecting the pregnancy.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".