Reproductive Parameters and Characteristics of Labrador Breed
Bibliographic record
Abstract
Abstracts: For a period of two years, a survey of 110 parturitions of Labrador breed bitches was carried out in Rosario City to determine the average number of puppies per litter, percentage of dystocia of all parturitions, percentage of males and females born and causes of dystocia, as well as the incidence of clinically detectable congenital pathologies.Over a total of 110 parturitions ninety three (84.5%)parturitions were normal and 17 (15.5%)were dystocic ones, it was established that atony 1 was the most numerous cause of dystocia (53%), followed by atony 2 (23%), dystocia by fetal disorders (6%), hydramnios (6%), uterine rupture (6%) and vagina fibrous band (6%).The whole puppies born was 866, 7.9 puppies per litter, if comparison between normal parturitions and dystocic ones is made, numbers are 7.05 and 8.02 respectively.When sex evaluation percentage is made 51.5% are males and 48.5% are females.When viability of puppies born is calculated it is established that in ones born by normal parturition perinatal death was 9.3%, while in those born by dystocia it was 10.9%.Among clinically verifiable congenital disorders low weight at birth was the most frequent congenital alteration, followed by cleft palate, lethal congenital edema or walrus syndrome and hare lip.
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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) | 0.002 | 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".