Impact of the Selection Criteria of Artificially Inseminated Cows on the Probability of Conception and the Occurrence of Embryonic Mortality in Senegal: Modeling Approach
Bibliographic record
Abstract
The objective of this study was to measure the relative influence of the animal’s age, body condition score (BCS), glycemia at day of insemination (D0) and the livestock management system on the probability of conception and occurrence of embryonic mortalities. In this study, 81 inseminated cows both of the Gobra Zebu breed and crossbred were sampled. Blood samples were collected the day of insemination (D0) and twenty-one (D21) and thirty-five (D35) days after AI. The BCS, the age and the livestock management system were recorded for all animals before the artificial insemination (AI). The glycemia was measured on D0. Pregnancy diagnosis was performed by progesterone and pregnancy associated glycoprotein assays and transrectal palpation. A multinomial logistic model was used to analyze the effect of the selection criteria for cows on the success rate of AI. The pregnancy diagnosis makes possible to detect 47% of pregnant cows and 26% of late embryonic mortality (LEM) at D60 post AI. The maximum likelihood test carried out on the model permits to reject the null hypothesis (p < 0.0001) according to which all animals have the same chance of being pregnant (47%). The variables thus provide a significant amount of information to explain the variability in the success rate of AI. The most significant factor was age (p < 0.0001), followed by the BCS, then glycemia and finally the livestock management system (p = 0.047). The BCS is the factor that most explains the variability in pregnant cows. For the group of LEM, age of the animal accounts for variability.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".