Effect of timing of artificial insemination on the prolificacy and sex ratio in canine species
Bibliographic record
Abstract
The importance of artificial insemination to canine reproduction is steadily increasing. On this regard, the knowledge of the factors that may affect success of the insemination techniques is determinant to obtain successful pregnancies and an adequate number of offspring per litter. Results archived in a four years database of reproduction visits of one veterinary hospital in Portugal were analyzed and complemented with questionnaires and phone calls to the bitches’ owners. Data consists of a total of 502 newborns from 86 whelping records from 52 bitches from ten different breeds aged 15 to 99 months monitored from breeding to whelping. In this database progesterone determinations were used to establish the day of LH surge; the later being used to estimate the gestational age. Delimitation of the bitch´s fertile period was based on the combined information from serum progesterone determinations and vaginal cytology. The day the plasma concentration of progesterone exceeded 2 ng /mL was considered the day of the LH surge. Two intravaginal inseminations with 48h of interval were performed on all females, with fresh semen from males with proven fertility. The objective of the present study was to determine whether different intervals between insemination and ovulation have an influence on the fertility, prolificacy and sex ratio in bitches. Three time periods of AI, relative to ovulation day, were used in this study and bitches were divided according into these three groups. Group A (early): AI was performed more than 24 hours before ovulation, n=11; group B (intermediate): AI was performed between 24 hours before and 24 hours after ovulation, n=34; group C (late): AI was performed more than 24 hours after ovulation, n=41. Factors that may affect secondary sex ratio and prolificacy in bitches were also determined using multiple regression analysis. Month and season of the year at insemination; breed, age at first heat, weight and age of bitches at AI, days after the onset of proestrus, duration of gestation, cesarean delivery, parity, embryonic and neonatal death, sexual behaviour at AI, vulvar anatomy, male’s experience, incidence of reproductive pathologies and whelping problems were analysed together. This study did not supported the hypothesis that changing the moment of artificial insemination in relation to ovulation affected secondary the litter size and the proportion of males and females at birth. However, the results showed that breed has an influence on prolificacy, gestation length, probability of having cesarean section and proportion of males and females at birth. Breeds with highest prolificacy are more likely to have longest gestation duration, high probability of having newborn females and less probability of having cesarean delivery. German Shepherd Dog had a greater likelihood of having longest gestation, followed by French Bulldog and Doberman, Labrador Retriever, Great Dane and Jack Russell, Bernese Mountain Dog, Pug and at least with shorter gestation duration and high probability of having cesarean delivery are Bull Terrier and English Bulldog. This information is clinically important in attempting to predict the whelping day and prepare a cesarean section. The importance for breeders lies in genetic improvement of the breed.
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 | 0.002 |
| 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 | 0.000 | 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".