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Record W2104897412 · doi:10.4141/cjas10016

Laying performance of six pure lines of chickens and four commercial hybrids at the Agassiz Research Centre

2010· article· en· W2104897412 on OpenAlexvenueno aff
F.G. Silversides

Bibliographic record

VenueCanadian Journal of Animal Science · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsnot available
FundersUniversity of New England
KeywordsHybridHeterosisBiologyAnimal scienceHorticulture

Abstract

fetched live from OpenAlex

The Agassiz Research Centre keeps six pure lines of high-producing but non-commercial layers. Egg production, body weight, feed consumption and efficiency, and several characteristics of the eggs were measured to 60 wk of age and compared with those of four commercial white- and brown-egg hybrids. Egg production from 20 to 60 wk was highest for the four commercial hybrids, with no difference between them, and was 6 to 11% lower for the pure line white-egg layers, and 7 to 13% lower for the pure line brown-egg layers. The pure line and hybrid white-egg layers had similar body weights, but the pure line brown-egg layers weighed more than the commercial brown-egg hybrid. Feed efficiency was similar for the four hybrids and was generally better for the hybrids than the pure lines, based largely on higher egg production rather than increased feed consumption. The difference between industrial hybrids and the pure lines studied can be attributed to the selection that has been applied as well as to heterosis. Key words: Chicken, genetic resources, Agassiz layer lines

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.054
GPT teacher head0.277
Teacher spread0.222 · 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 source (direct Gemma or distilled Codex), 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

Citations7
Published2010
Admission routes1
Has abstractyes

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