Precision dairy technology and its association with estrous expression and fertility outcomes in Holstein cattle
Bibliographic record
Abstract
Technologies developed to detect estrus have the potential to improve dairy cattle fertility; however, there is still need for better understanding of how the readers of automated activity monitors (AAM) are associated with fertility outcomes and reproductive physiology. The objectives of this thesis were to determine 1) if estrous expression, detected by AAM can be used within an ovulation synchronization program for timed artificial insemination (AI) and embryo transfer (ET), and 2) the relationship between estrous expression, ovulation rates, viability and quality of embryo, progesterone and estradiol concentrations, and fertility and pregnancy loss. In Chapter 2, it was demonstrated that following the use of a timed AI synchronization protocol, cows showed different intensities of estrus detected by an AAM and that greater intensity of estrous expression resulted in greater fertility and decreased pregnancy losses. In Chapter 3, I investigated the intensity of estrous expression at the end of a superovulation protocol, for embryo collection, in Holstein heifers and its association with production and viability of embryo. Heifers with greater intensity of estrus, detected by an AAM or a breeding indicator, had greater percentage and number of viable embryos compared with animals that had lower intensity of estrus. In Chapter 4, I examined the association between estrous expression and the success of ET, and found that the intensity of estrous expression, and the occurrence of estrus prior to ET, improved fertility. Finally, in Chapter 5, I summarized work that investigated if the concentrations of progesterone (P4) around estrus is related to the intensity of estrus and fertility of Holstein cows. Greater concentrations of P4 and lower concentrations of E2 at AI were associated with lower intensity estrous, while greater concentrations of P4 prior to AI were associated with greater estrous expression and fertility. Future research is needed to further understand the association of intensity estrous expression and fertility and pregnancy loss.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".