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Record W7095439174

HETEROSIS, DIRECT AND MATERNAL ADDITIVE EFFECTS ON RABBIT GROWTH AND CARCASS CHARACTERISTICS

2015· article· en· W7095439174 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRabbits: Nutrition, Reproduction, Health
Canadian institutionsnot available
Fundersnot available
KeywordsCrossbreedBreedCarcass weightBody weightWeaningHindlimbLoin
DOInot available

Abstract

fetched live from OpenAlex

A total of 142 male and female rabbits of two breeds, Californian (CA) and New-Zealand White (NZ), and their reciprocal crosses were used. This study aimed to estimate heterosis, direct and maternal additive effects as well as some non genetic effects on rabbit growth and carcass characteristics in order to identify the best crossbreeding plan to use for rabbit meat production under Quebec conditions. Kits used for this experiment were weaned at 5 weeks of age. Each rabbit was identified and weighed individually at weaning and at 63 days of age. During the fattening period, rabbits were placed in individual cages. Rabbits were slaughtered after 18 h fasting from feeds only. The commercial carcass including liver, kidneys and perirenal fat was weighed after 2 hours chilling at 4°C. After dissection, fore part, intermediate part and hind part of carcass were measured. Dressing out percentage was calculated as chilled carcass weight x 100/live weight. One of the hind legs was used to evaluate meat/bone ratio. Statistical analyses were performed using the procedure GLM of SAS. Results showed significant differences between breed types for individual live weight at 35 d, average daily gain, live weight at 63 d, fore part, intermediate part and hind part yields. Overall, for growth performances (ADG and live weight at 63 d) and hind part yield, breed types from NZ dams

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.224
Threshold uncertainty score0.206

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 head0.019
GPT teacher head0.227
Teacher spread0.208 · 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 designObservational
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

Citations0
Published2015
Admission routes1
Has abstractyes

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