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Record W3198442480 · doi:10.21423/aabppro20153598

Decay of passive antibodies in calves fed maternal colostrum or a colostrum replacer

2015· article· en· W3198442480 on OpenAlexaff
B. Stampfl, S. Godden, D. Haines

Bibliographic record

VenueAmerican Association of Bovine Practitioners Conference Proceedings · 2015
Typearticle
Languageen
FieldVeterinary
TopicAnimal health and immunology
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsColostrumAntibodyPassive immunityImmune systemImmunologyImmunoglobulin GVaccinationMedicineBiology

Abstract

fetched live from OpenAlex

The passive transfer of immunoglobulins to neonatal calves via maternal colostrum is a major determinant of the calf’s health early in life. It is important that each calf receive 150-200g immunoglobulin G (IgG) within two hours of birth. However, maternal colostrum (MC) can be highly variable in its IgG content and specific antibody levels. Colostrum replacers (CR) can substitute for MC and may provide a more consistent dose of both IgG mass and specific antibodies. Since high levels of colostral antibodies can interfere with the humoral immune response to vaccination, knowing the time that calves become seronegative could allow for strategic timing of vaccination to produce a more consistent protective humoral immune response. The first objective of this study was to compare the level and persistence (antibody decay curves) for specific antibodies in calves fed either MC or CR. A second objective was to investigate whether feeding a CR (vs MC) would result in a more consistent time to seronegativity, with less calf-to-calf variability. We hypothesized that both forms of colostrum would transfer similar mean levels of antibodies, but that CR would deliver a more consistent time to seronegativity for diseases of interest.

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)0.002
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 score0.072
Threshold uncertainty score0.704

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.049
GPT teacher head0.345
Teacher spread0.296 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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