MétaCan
Menu
Back to cohort
Record W2883043110 · doi:10.3168/jds.2018-14390

Effect of age of introduction to an automated milk feeder on calf learning and performance and labor requirements

2018· article· en· W2883043110 on OpenAlexafffund
Catalina Medrano-Galarza, S.J. LeBlanc, T.J. DeVries, Andria Jones‐Bitton, J. Rushen, A.M. de Passillé, M.I. Endres, Derek B. Haley

Bibliographic record

VenueJournal of Dairy Science · 2018
Typearticle
Languageen
FieldVeterinary
TopicAnimal health and immunology
Canadian institutionsUniversity of British ColumbiaUniversity of Guelph
FundersDairy Farmers of CanadaUniversity of Guelph
KeywordsColostrumStarterAnimal scienceMedicineBiologyFood scienceImmunology

Abstract

fetched live from OpenAlex

Group housing of dairy calves with automated milk feeders (AMF) is increasingly being used, but the effect of introducing calves to the AMF at a very young age (<24 h) on calf performance, health, and welfare, as well as farm personnel labor requirements are unknown. The objective of this controlled trial was to investigate whether early (<24 h after birth) introduction of calves affects the time to learn how to drink from the AMF, labor requirements for feeding milk during the learning phase, and average daily gain during the milk-feeding period compared with calves conventionally introduced at 5 d of age. Sixty Holstein calves (heifers and bulls) were assigned at birth to either early introduction (<24 h after birth) or conventional introduction (at 5 d of age) to the group pen with AMF. After birth, calves were housed in individual pens and then introduced, based on assigned treatment, to the group pen with an AMF and a continuous flow stocking approach. Calves were fed milk replacer and gradually weaned from d 47 to 60 of age. Calves had access to starter from 5 d of age, and to water and straw right after colostrum feeding. We measured the time between first training to use the AMF and first unassisted visit to the AMF with milk intake, the number of assisted visits until the calf was independent in its use of the AMF (successful learning), and the total time required for milk feeding (labor) until successful learning. Calves were weighed at birth, 30, 46, and 61 d of age, and were monitored daily for signs of disease. Daily milk and starter intake per calf were automatically recorded. Early-introduced calves took longer to successfully learn to use the AMF {64.9 h [95% confidence interval (CI) = 59.1 to 77.9] vs. 31.4 h (95% CI = 22.8 to 47.9)} and tended to require more assisted visits [7.8 visits (95% CI = 6.2 to 9.7) vs. 5.9 visits (95% CI = 4.8 to 7.5)] compared with conventionally introduced calves. Labor for milk feeding was greater for conventionally introduced calves relative to early-introduced calves [145.6 min (95% CI = 125.1 to 169.4) vs. 39.9 min (95% CI= 33.5 to 47.6)]. Disease risk was similar between treatments but the risk of severe versus mild diarrhea was greater for early- compared with conventionally introduced calves (odds ratio = 4.7; 95% CI 1.01 to 31.1). Early-introduced calves consumed less milk during the first days of life compared with conventionally introduced calves (d 2 = 5.5 vs. 6.4 L; d 3 = 7.0 vs. 8.2 L; d 4 = 7.0 vs. 8.4 L; d 6 = 6.4 vs. 7.9 L; d 7 = 6.0 vs. 7.0 L, respectively), with no differences after 8 d. We found no effect of treatment on average daily gain. Although introducing calves <24 h after birth required more assistance to use the AMF, farm labor for milk feeding tasks was less for early-introduced calves. Thus, with early introduction to AMF, a trade-off may exist between reduced labor per calf, with no effect on weight gain, but potentially a higher risk of severe diarrhea (vs. mild).

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)0.002
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.030
GPT teacher head0.377
Teacher spread0.348 · 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
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Dairy ScienceSame topicAnimal health and immunologyFrench-language works237,207