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Maximization of Lactation Milk Production Without Decreasing Persistency

2005· article· en· W2162065459 on OpenAlexaff
C.Y. Lin, Kenji Togashi

Bibliographic record

VenueJournal of Dairy Science · 2005
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsLactationSelection (genetic algorithm)Index (typography)MathematicsAnimal scienceTraitStatisticsBiologyGeneticsPregnancyComputer science

Abstract

fetched live from OpenAlex

This study treats each daily estimated breeding value (EBV) of the lactation as a separate trait to modify the lactation curve on a daily basis. Six selection strategies for improving lactation milk without decreasing persistency were compared: 1) index I(R1), subject to the restriction of equal genetic gains at days in milk (DIM) 60 and 280, 2) I(R2), subject to the restriction of zero gain at DIM 60, 3) desired gains index I(d), designed to increase lactation milk without altering the lactation curve, 4) index I(u), comprising lactation EBV and persistency without standardization, 5) index I(w), consisting of lactation EBV (EBV(L)) and persistency with standardization, and 6) conventional selection on EBV(L) and used as a basis for comparison. Of the 6 selection strategies compared, I(R2) yielded the greatest persistency, but achieved the smallest response in EBV(L), suggesting that it is impractical to increase persistency by inhibiting change in the peak yield. Index I(u) showed the same response in lactation milk as conventional selection on EBV(L), but resulted in the same decreased persistency. Although both I(R1) and I(d) achieved constant persistency, the former produced a greater lactation response (669 kg EBV) than the latter (560 kg EBV). Thus, I(R1) is a viable strategy for improving EBV(L) while holding persistency constant. None of the 6 selection strategies excelled in both lactation milk and persistency. Index I(w) appears to be a reasonable choice for improving both traits, although responses would depend on the relative economic importance of the 2 traits. Differential responses between I(u) and I(w) emphasize the need to weight the EBV of different traits by the inverse of their standard deviations in index construction when the EBV vary widely in variance. The general formula developed here provides a useful genetic means of modifying the lactation curve by restricting differential genetic gains among different days of the lactation.

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)0.000
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.013
GPT teacher head0.256
Teacher spread0.243 · 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

Citations21
Published2005
Admission routes1
Has abstractyes

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