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Record W2516507953 · doi:10.1017/s0022029916000285

Lying behaviour and IgG-levels of newborn calves after feeding colostrum via tube and nipple bottle feeding

2016· article· en· W2516507953 on OpenAlexaff
Stephanie Bonk, Audrey Nadalin, W. Heuwieser, D. M. Veira

Bibliographic record

VenueJournal of Dairy Research · 2016
Typearticle
Languageen
FieldVeterinary
TopicAnimal health and immunology
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsColostrumBottleAnimal sciencePlaceboMedicineBiologyAntibodyImmunologyMaterials science

Abstract

fetched live from OpenAlex

Oesophageal tube feeding colostrum is used to ensure sufficient colostrum intake in newborn calves but the impact of tube feeding on animal behaviour is unclear. Therefore the objective of this study was to compare lying behaviour of tube-fed or bottle-fed dairy calves. Calves (n = 37) in 3 groups were offered 3·5 l colostrum 2 h after birth. Calves of the bottle group were fed with a nipple bottle. Calves of the placebo tubing group were tubed for 4 min but no colostrum was given and they were then fed with a nipple bottle. Calves of the tubing group received 3·5 l colostrum via tube feeding. Consumed amount of bottle and placebo tubing calves was recorded. If they refused some of the offered 3·5 l the rest was offered in a second feeding 2 h later. Lying behaviour was measured by data loggers fitted to right hind leg for 3 d. Blood samples were taken 24 h after birth for determination of IgG concentration. The voluntary colostrum intake differed significantly between bottle-fed and placebo tubed calves at first feeding. Considering both colostrum feedings, bottle-fed calves consumed 3·44 ± 0·14 l and placebo tubed calves consumed 3·20 ± 0·38 l colostrum. ImmunoglobulinG intake (255·6 ± 77·5 g IgG), serum IgG concentration 24 h after birth (22·8 ± 6·7 g/l) and total serum protein concentration (6·1 ± 0·6 g/dl) did not differ between groups. None of the calves had a failure of passive transfer. There was no effect of tubing on lying behaviour.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.478
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 integrity0.0000.001
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 head0.153
GPT teacher head0.423
Teacher spread0.270 · 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

Citations9
Published2016
Admission routes1
Has abstractyes

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