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Record W4394739248

Comparison of virus-neutralizing and virus-specific ELISA antibody responses among bovine neonates differentially primed and boosted against bovine coronavirus.

2024· article· en· W4394739248 on OpenAlexaff
Nathan Erickson, Stacey Lacoste, Michelle Sniatynski, Cheryl Waldner, John Ellis

Bibliographic record

VenuePubMed · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Virus Infections Studies
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsBovine coronavirusAntibodyVirologyBiologyBovine respiratory diseaseCoronavirusVirusNeutralizing antibodyImmunologyMedicineCoronavirus disease 2019 (COVID-19)DiseaseInfectious disease (medical specialty)Internal medicine
DOInot available

Abstract

fetched live from OpenAlex

Objective: This study addressed the current gap in knowledge of neonatal prime-boost immune responses for the control of bovine coronavirus (BCoV) respiratory disease in weaning-age beef cattle. Animals: Study 1 and Study 2 had 33 and 22 commercial cross neonatal beef calves, respectively. Procedures: Study 1 compared BCoV-neutralizing antibody concentrations of control calves with 3 groups of calves differentially vaccinated with mucosal and/or systemic BCoV modified live virus (MLV) vaccines. Study 2 compared specific and neutralizing antibody concentrations among mucosally BCoV primed groups of calves that were differentially systemically boosted. Results: In Study 1, calves that were mucosally primed and systemically boosted had higher BCoV-neutralizing antibody concentrations than the control group at weaning. In Study 2, boosting mucosally primed calves by injecting inactivated or MLV vaccine resulted in anamnestic BCoV-specific antibody responses at weaning. Conclusion: Neonatal mucosal priming and systemic boosting resulted in anamnestic BCoV antibody responses at weaning. Clinical relevance: Prime-boost vaccination should be considered for control of BCoV respiratory disease.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.651
Threshold uncertainty score0.508

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 studies0.0000.000
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 head0.061
GPT teacher head0.296
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 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

Citations2
Published2024
Admission routes1
Has abstractyes

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