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Record W2802712494 · doi:10.3168/jds.2017-14153

Association of quarter milking measurements and cow-level factors in an automatic milking system

2018· article· en· W2802712494 on OpenAlexaboutno aff
J.F. Penry, P.M. Crump, Laura L. Hernandez, Douglas J. Reinemann

Bibliographic record

VenueJournal of Dairy Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMilk Quality and Mastitis in Dairy Cows
Canadian institutionsnot available
FundersUniversity of Wisconsin-Madison
KeywordsMilkingUdderQuarter (Canadian coin)Animal scienceAutomatic milkingLactationHerdMilk productionMathematicsBiologyMastitisGeographyPregnancyIce calving

Abstract

fetched live from OpenAlex

The primary aim of this observational study, in a single herd milked using multiple automatic milking system units, was to describe associations of quarter milk yield variability and quarter peak milk flow rate with cow-level factors. Information from the current lactation of 1,549 primiparous and multiparous cows was collected from January to December 2015. Data from each individual milking used in the analysis included quarter milk yield (QMY), udder milk yield, quarter peak milk flow rate (QPMF), quarter average milk flow rate (QAMF), quarter milking time, and milking interval. Milking interval and milk yield were used to calculate milk production rate (kg/h) at the quarter and udder levels. We investigated associations between QPMF and milking interval, QPMF and days in milk, and QMY and QAMF. A strong association between QPMF and both QAMF and milking interval was observed. A moderate association was found between QPMF and stage of lactation. However, QMY was not a useful indicator of QPMF because of the weak association observed between these variables. In this study, rear quarter QPMF was significantly increased by 3% compared with front quarter QPMF (1.45 vs 1.41 kg/min). Quarter milk yield was calculated as a percentage contribution of total udder milk yield per 10-d in milk window and ranked from lowest to highest contribution. Quarter contribution to udder milk yield showed a high level of variability, with 39% of animals having all 4 quarters change contribution rank at least once during part of or the whole lactation. Only 14% of cows were observed to have no change in quarter rank. When quarter contribution was assessed, irrespective of physical position of quarter within the udder, the percent of highest to lowest contribution across the lactation was relatively stable. The standard deviation of quarter milk production rate for each cow was regressed against the same cow's peak udder milk production rate, within a lactation, to ascertain whether quarter milk production rate variance could be used to predict peak udder milk production rate. Knowledge of the intra-udder quarter milk production rate standard deviation for an individual cow is not useful in predicting peak udder milk production rate. Quarter milking time appears to be a useful indicator to predict the optimal order of teatcup attachment. Analysis from this large, single-herd population indicates that QPMF is associated with the cow-level factors milking interval and days in milk, and that intra-udder QMY is highly variable.

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)0.003
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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.080
GPT teacher head0.283
Teacher spread0.203 · 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

Citations22
Published2018
Admission routes1
Has abstractyes

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