Seasonal and breed effects on reproductive parameters in bitches in the tropics: a retrospective study
Bibliographic record
Abstract
OBJECTIVES: To investigate the influence of season and breed on reproductive parameters in bitches raised under tropical climatic conditions. METHODS: Over a seven year period, from 1998 to 2004, 310 oestrous periods of 53 bitches were observed. The dogs were of various breeds; dobermann (number of bitches/number of oestrous cycles) (n=2/19), German shepherd dog (n=35/211), Labrador retriever (n=14/68) and Rottweiler (n=2/12). In 250 of the 310 oestrous periods, natural matings took place on days 9 and 11 after the onset of pro-oestrus. The whelping rate was analysed for bitches of each breed. Variables, including breed and the whelping rate, by month of the year, were used for analysis of the inter-oestrus interval, gestation length, total number of pups born, number of live pups born and the weight of the pups at birth. RESULTS: A low frequency of oestrous activity was found during the summer. Breeding dogs in the summer resulted in a low whelping rate. No difference (P>0.05) was seen in the whelping rate of each breed: dobermann (70.5 per cent), German shepherd dog (61.5 per cent), Labrador retriever (67.9 per cent) and Rottweiler (100 per cent). The Labrador retriever had a longer inter-oestrus interval (252 [114] and 190 [61] days) (P<0.01) and a larger litter size (8.2 [1.8] and 6.6 [2.8]) (P<0.05) than the German shepherd dog. CLINICAL SIGNIFICANCE: The environmental factors in summer tend to reduce oestrus incidence and fertility in the bitches. According to litter size, the Labrador retriever seems to have a more efficient reproductive performance than the German shepherd dog. The Labrador retriever had a longer inter-oestrus interval than the German shepherd dog.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".