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Short Communication: Diurnal Feeding Pattern of Lactating Dairy Cows

2003· article· en· W2135519364 on OpenAlexafffund
T.J. DeVries, M.A.G. von Keyserlingk, K. A. Beauchemin

Bibliographic record

VenueJournal of Dairy Science · 2003
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEffects of Environmental Stressors on Livestock
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMilkingMorningAnimal scienceEveningBarnAutomatic milkingDiurnal temperature variationAttendanceAlleyBiologyLactationGeographyBotanyMeteorologyPregnancy

Abstract

fetched live from OpenAlex

The objectives of this research were to: 1) describe the diurnal variation in feed alley attendance patterns of lactating dairy cows, 2) describe the sources of variation in these patterns, and 3) determine the effects of altering the feed push-up schedule on these patterns. An electronic monitoring system was used to record individual cow presence (6-s resolution) at the feed alley for 24 cows housed in a free-stall barn. Cows were subjected to 2 feeding schedules: 1) baseline schedule, where cows were fed at 0600 and 1515 h and feed was pushed closer to the cows at 1100 and 2130 h; and 2) experimental schedule, where 2 additional feed push-ups at 0030 and 0330 h were added to the baseline schedule. With the data collected from the monitoring system, description of the feed alley attendance patterns on a per minute basis of the group of cows was undertaken. Feed alley attendance was consistently higher during the day and early evening compared with the late night and early morning hours. The greatest percentage of cows attending the feed alley was seen after the delivery of fresh feed and the return from milking. The addition of extra feed push-ups in the early morning hours did little to increase feeding activity. It can be concluded that milking and delivery of fresh feed had a much greater affect on the diurnal pattern of feed alley attendance than did the feed push-ups.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.688
Threshold uncertainty score0.233

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.023
GPT teacher head0.254
Teacher spread0.230 · 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 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

Citations198
Published2003
Admission routes2
Has abstractyes

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