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Record W4250742827 · doi:10.4141/cjas07131

Evaluation of sheep genetic resources in North America: Ewe productivity of purebred, crossbred and synthetic populations

2008· article· en· W4250742827 on OpenAlexvenueno aff
J.N.B. Shrestha, W. J. Boylan, W. E. Rempel

Bibliographic record

VenueCanadian Journal of Animal Science · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsnot available
Fundersnot available
KeywordsBreedPurebredFecundityBiologyAnimal scienceCrossbreedProductivityWoolVeterinary medicinePopulationDemographyGeography

Abstract

fetched live from OpenAlex

Ewe productivity from divergent genetic types of established purebreds e.g., Dorset (D), Lincoln (L), Rambouillet (Ra), Suffolk (Su) and Targhee (T); fecund-type breeds e.g., Finnsheep (F) and Romanov (Ro) breeds, and their reciprocal crosses; the Outaouais (O) and Rideau (R) Arcott breeds and their reciprocal crosses; and Synthetic I (½ F, ½ L), Synthetic II (½ D, ½ Ra) and Synthetic III (¼ F, ¼ L, ¼ D, ¼ Ra) populations were evaluated. Prolificacy, fecundity, ewe weight, grease fleece weight, wool grade, lamb survival and total lamb weights at birth, 30 d and 140 d per ewe lambing were considered jointly as a measure of ewe productivity. In general, fecund-type breed cross and Arcott breed cross were highest in productivity, the fecund-type breed, Arcott breed and synthetic populations were intermediate, in contrast the established breeds was lowest. Regardless of significantly heavier ewe and grease fleece weights, and superior wool grade of the established breeds, their poor performance can be attributed to lower prolificacy, fecundity and total lamb weights (P < 0.05). Within established breeds, the D, L, Ra and T breeds were comparable in productivity to the Su breed, but lower than the F breed (P < 0.05). The Ro breed surpassed the F breed because of significantly higher prolificacy and fecundity, and heavier ewe weight and total lamb weights, while grease fleece weight and wool grade were inconsistent. The F and Ro breeds were comparable to the Su breed despite significantly higher prolificacy and fecundity, in contrast to lighter ewe and grease fleece weights, lower wool grade, lamb survival, similar total lamb weights. Although the R breed produced significantly heavier grease fleece weight and total lamb weights than the O breed, both breeds were similar to the Su breed despite their significantly higher prolificacy and fecundity, in contrast to similar ewe weight, wool grade, lamb survival and total lamb weights, and lighter grease fleece weight. The O and R breeds were more productive than the F breed as a result of significantly heavier ewe weight, superior wool grade, higher lamb survival and heavier total lamb weights, despite their similar prolificacy, fecundity and grease fleece weight. Synthetic I and Synthetic III with Finnsheep lineage surpassed Synthetic II in productivity as a result of significantly higher prolificacy and fecundity, and heavier total lamb weights, despite similar ewe and grease fleece weights and inconsistent wool grade and lamb survival. At the same time, Synthetic I and Synthetic III were not only similar to the Su breed, but surpassed the F breed, whereas Synthetic II was similar to the Su breed and lower than the F breed. Finally, systematic crossbreeding and composite population of complementary fecund-type and established breeds achieved increased productivity from additive genetic variation and heterosis. Key words: Reproductive performance, ewe and grease fleece weights, total lamb weights, Arcotts, Finnsheep, Romanov, synthetic populations, established breeds

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.033
GPT teacher head0.261
Teacher spread0.228 · 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

Citations2
Published2008
Admission routes1
Has abstractyes

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