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Record W3133291045 · doi:10.1139/cjas-2020-0149

Effects of colostrum management on transfer of passive immunity and the potential role of colostral bioactive components on neonatal calf development and metabolism

2021· article· en· W3133291045 on OpenAlexaffvenue
A.J. Fischer-Tlustos, A. Lopez, Koryn S Hare, Katharine M Wood, M.A. Steele

Bibliographic record

VenueCanadian Journal of Animal Science · 2021
Typearticle
Languageen
FieldVeterinary
TopicAnimal health and immunology
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsColostrumPassive immunityImmunityNarrative reviewImmune systemBiologyBiotechnologyAntibodyMedicineImmunologyIntensive care medicine

Abstract

fetched live from OpenAlex

Neonatal dairy and beef calves are required to ingest adequate volumes of high-quality colostrum during their first hours of life to acquire transfer of passive immunity (TPI). As such, immunoglobulin G (IgG) has largely been the focus of colostrum research over recent decades. Yet, little is known about the additional bioactive compounds in colostrum that potentially influence newborn calf development and metabolism. The purpose of this narrative review is to synthesize research regarding the effects of colostrum management practices on TPI, as well as to address the potential role of additional colostral bioactive molecules, including oligosaccharides, fatty acids, insulin, and insulin-like growth factor-I, in promoting calf development and metabolism. Due to the importance of IgG in ensuring calf immunity and health, we review past research describing the process of colostrogenesis and dam factors influencing the concentrations of IgG in an effort to maximize TPI. We also address the transfer of additional bioactive compounds in colostrum and prepartum management and dam factors that influence their concentrations. Finally, we highlight key areas of future research for the scientific community to pursue to ultimately improve the health and welfare of neonatal dairy calves.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.863
Threshold uncertainty score0.471

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.014
GPT teacher head0.248
Teacher spread0.234 · 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 designBench or experimental
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

Citations58
Published2021
Admission routes2
Has abstractyes

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