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Record W2942918928 · doi:10.1017/s0022029919000220

Milk β-hydroxybutyrate concentration measured by Fourier-transform infrared and flow-injection analyses from samples taken at different times relative to milking

2019· article· en· W2942918928 on OpenAlexaff
M. Duplessis, D.E. Santschi, S. Plante, Camille Bergeron, Daniel Lefebvre, J. Durocher, R.I. Cue

Bibliographic record

VenueJournal of Dairy Research · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsSte. Anne's HospitalUniversité LavalMcGill UniversityValacta (Canada)
Fundersnot available
KeywordsMilkingChromatographyFourier transformChemistryAnalytical Chemistry (journal)InfraredFlow (mathematics)Fourier transform infrared spectroscopyEnvironmental scienceAnimal scienceMathematicsBiologyOpticsPhysics

Abstract

fetched live from OpenAlex

Analysis of milk BHB concentration by Fourier-transform infrared (FTIR) spectrometry more frequently than regular milk testing could help dairy producers in decision making, particularly if it would be possible to use small hand-stripped samples (hereinafter simply called samples) taken between dairy herd improvement (DHI) test-samples analysed using DHI algorithms. The aim of this Research Communication was to evaluate milk BHB concentration and the prevalence of elevated milk BHB concentration analysed by FTIR spectrometry compared with flow-injection analysis (SKALAR) from samples taken at different times relative to the milking. A total of 293 early-lactation cows in 44 commercial dairy herds were involved in the study. Herds were visited once during the morning milking when a routine DHI test-sample was obtained using in-line milk samplers. Additional milk samples were taken by hand stripping as follows: (1) Just before connecting the milking machine; (2) immediately after removing the milking machine; (3) 3 h after milking and (4) 6 h after milking. Milk samples were analysed for BHB concentration by FTIR and SKALAR, the latter being the reference method. Milk BHB concentration from samples taken before milking was different between FTIR and SKALAR whereas no difference was noted for other sampling times, although milk BHB concentration rose as time after milking increased. Except for DHI test-samples for which prevalence was not different between analysis methods, prevalence of elevated milk BHB concentration (≥0.15 mmol/l) was greater for FTIR analysis. However, no difference in prevalence was observed between SKALAR and FTIR when using a threshold of ≥0.20 mmol/l. In summary, hand-stripped milk samples taken any time after removing the milking machine until 6 h after the milking can be recommended for FTIR analysis of elevated milk BHB concentration prevalence provided a threshold of 0.20 mmol/l is used.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.134
Threshold uncertainty score0.451

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 head0.053
GPT teacher head0.334
Teacher spread0.281 · 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 designBench or experimental
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

Citations3
Published2019
Admission routes1
Has abstractyes

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