Impact and mechanisms of inflammatory diseases on embryonic development and fertility in cattle
Bibliographic record
Abstract
Inflammatory diseases are prevalent in cattle and impair fertility.Cows affected by inflammatory disease from parturition to the day before breeding have reduced fertilization of oocytes, reduced survival of zygotes to the morula stage, impaired development to early stages of conceptus elongation, reduced secretion of interferon during the period of pregnancy recognition, altered transcriptome of preimplantation conceptus cells, and increased pregnancy loss.Consequently, these cows have reduced pregnancy and calving per breeding.Reduced oocyte competence is a likely reason for the carryover effects of diseases on developmental biology, but impaired uterine environment is also involved.Effects on pregnancy survival are observed up to 5 months after the diagnosis and treatment of disease, and effects on developmental biology seem to be extended into postnatal life in pregnancies that survive until term.Although the biological mechanism mediating the effects of inflammatory diseases are still not completely understood, control of inflammation during the clinical presentation of diseases seems to alleviate the negative effects on reproductive biology.It is increasingly evident that animal health, not only at the time of breeding or pregnancy development but also in the period preceding breeding, is imperative for optimal reproduction in cattle and should always be considered in herd evaluations and managerial decisions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".