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Reproductive Parameters and Characteristics of Labrador Breed

2018· article· en· W2894089471 on OpenAlexaboutno aff
Carlos Sorribas, Mónica Pirles, Laura Schiaffino, Carlos Pagni

Bibliographic record

VenueJournal of Pharmacy and Pharmacology · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Genetics and Reproduction
Canadian institutionsnot available
Fundersnot available
KeywordsBreedBiologyGeographyZoologyAnimal science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.325

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.309
Teacher spread0.291 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2018
Admission routes1
Has abstractyes

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