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Record W1974898737 · doi:10.3168/jds.2012-6148

Development of a genetic evaluation for body condition score for Canadian Holsteins

2013· article· en· W1974898737 on OpenAlexafffundabout
S. Loker, Catherine Bastin, F. Miglior, A. Sewalem, L.R. Schaeffer, J. Jamrozik, V.R. Osborne, A. K. A. Ali

Bibliographic record

VenueJournal of Dairy Science · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaUniversité de LiègeUniversity of Guelph
KeywordsLactationHerdAnimal scienceTraitBiologyDaughterStatisticsDemographyMathematicsGeneticsPregnancyEvolutionary biology

Abstract

fetched live from OpenAlex

Valacta (Sainte-Anne-de-Bellevue, Québec, Canada) is the Canadian Dairy Herd Improvement organization responsible for milk recording in Québec and Atlantic provinces. Up to 14 first-lactation body condition score (BCS) records were collected per cow (average of 2.5 records per cow), allowing the trait to be described by a random regression animal model so that animals could be ranked by the shape of their BCS curve. However, Valacta's BCS are available from Québec herds only and the long-term objective of this research is to develop a nationwide genetic evaluation of sires and cows for BCS. Alternatively, Holstein Canada (Brantford, Ontario, Canada) collects type trait records nationwide, primarily for first-lactation cows. Holstein Canada typically collects a single record per trait, so that selection for Holstein Canada BCS would be based on overall BCS level rather than the shape of the BCS curve. Several different methods of genetically evaluating Valacta's BCS were investigated, including consideration of average BCS level across lactation, the amount of fluctuation in the BCS curve during lactation, and combinations of BCS level and BCS fluctuation. Sires with ≥ 25 daughters were compared (as opposed to comparing cows) because their BCS estimated breeding values (EBV) are based on more information, and so should be more reliable. Of the different methods of calculating Valacta BCS EBV, ranking bulls based on overall BCS level gave the best results in that their daughter phenotypic BCS curves showed limited loss in early lactation BCS and replenished condition by the end of lactation. Whereas Valacta's BCS were analyzed using a random regression animal model, Holstein Canada only needs to collect 1 BCS record per cow at classification and the resulting BCS EBV was strongly correlated with Valacta's BCS EBV. Furthermore, because Holstein Canada's BCS are collected nationally and Valacta's BCS are not, a national genetic evaluation for Holstein Canada's BCS is more convenient. The results of this study do not eliminate the possibility of a genetic evaluation of BCS as a longitudinal trait, but indicate that other methods of calculating Valacta BCS EBV should be explored. Until that time, genetically evaluating Holstein Canada's BCS is simple, easily implemented, and may be effective in altering the level and shape of the genetic BCS curve.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.135
Threshold uncertainty score0.271

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.027
GPT teacher head0.288
Teacher spread0.262 · 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

Citations4
Published2013
Admission routes3
Has abstractyes

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