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Record W2789370314 · doi:10.3168/jds.2017-14011

Effect of stall design on dairy calf transition to voluntary feeding on an automatic milk feeder after introduction to group housing

2018· article· en· W2789370314 on OpenAlexafffund
Tanya Wilson, S.J. LeBlanc, T.J. DeVries, Derek B. Haley

Bibliographic record

VenueJournal of Dairy Science · 2018
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsUniversity of Guelph
FundersMinistry of Agriculture, Food and Rural AffairsOntario Ministry of Agriculture, Food and Rural AffairsDairy Farmers of Canada
KeywordsStall (fluid mechanics)Animal scienceDairy industryBiologyTurnoverFood scienceEngineeringEconomics

Abstract

fetched live from OpenAlex

Automatic milk feeders (AMF) for young dairy calves are widely used in the dairy industry. These feeders are thought to have benefits for calf health and welfare and may reduce labor required for feeding; however, little is known about how calves adapt to feeding with AMF. The objective of this study was to observe the effects of feeding stall design on calves learning to use the AMF. The hypothesis was that solid side stalls, compared with steel bar stalls, would result in a longer latency to approach and feed from the AMF without assistance. A total of 147 Holstein calves (80 male and 67 female) were enrolled at 4 d of age, introduced to a group pen, and, at the same time, trained on an AMF. For training, calves were allowed to suck on the trainer's fingers and guided to the teat. Calves were allocated to 1 of 2 stall designs at the pen level, depending on which treatment cohort they were born into, either with steel bar stall walls (n = 46 male, 34 female calves) or with solid side stall walls (n = 34 male, 33 female calves). For 72 h after introductory training on the AMF, data from the feeders were collected and calf behavior was monitored by video. Outcomes measured included latency to first voluntary visit to the feeder and to first feeding, time spent in the feeder, amount of milk consumed over 72 h, number of retraining sessions required (retrained if <2 L was consumed every 12 h), and exploratory behavior, such as sniffing and licking of the feeder. Data were analyzed using mixed effects linear regression models or a Poisson model for the outcome of retraining. For certain outcomes the effects of stall design interacted with difficulty of training (willingness to enter feeder and drink); for the 38% of calves that were scored as moderately difficult to train on a scale of easy, moderate, or difficult, treatment (stall design) differences were detected. These calves took 2× longer to lick or bite toward the nipple, 2× longer to first voluntarily feeding, and consumed less milk over 72 h following training when trained on the steel bar stall design. These results suggest simple features of a stall may influence how quickly calves learn to use an AMF, but that the influence of stall wall design was affected by how easy calves were to train on the feeder upon initial introduction, which may depend in part on certain aspects of calf temperament. For many calves, solid side stalls at an AMF resulted faster in adaption than the steel bar stalls.

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)0.001
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.032
GPT teacher head0.329
Teacher spread0.297 · 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

Citations6
Published2018
Admission routes2
Has abstractyes

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