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Record W4396658463

Temporal patterns of bovine leukemia virus infection in dairy herds in Atlantic Canada.

2024· article· en· W4396658463 on OpenAlexafffundabout
Emily John, M. Cameron, Henrik Stryhn, Greg Keefe, J. Trenton McClure

Bibliographic record

VenuePubMed · 2024
Typearticle
Languageen
FieldImmunology and Microbiology
TopicT-cell and Retrovirus Studies
Canadian institutionsUniversity of Prince Edward Island
FundersAtlantic Veterinary CollegeDairy Farmers of Nova Scotia
KeywordsHerdBovine leukemia virusVirologyLeukemiaVirusBiologyGeographyZoologyEcologyImmunology
DOInot available

Abstract

fetched live from OpenAlex

Objective: The primary objective was to determine the youngest age group where bovine leukemia virus (BLV)-infected dairy animals were identified. The secondary objective was to investigate associations between age-specific management practices and BLV infection status of different age groups of dairy calves and heifers. Procedure: For enrolled herds, BLV status was determined using blood samples from pre-weaned calves, weaned calves, and breeding-age heifers; and bulk tank milk from the adult herd. A questionnaire investigating age-specific management factors was administered for each herd. Ordinal logistic regression was performed to identify management factors associated with the youngest age range in which BLV was identified. Results: Fifty-three dairy herds from the 4 provinces in Atlantic Canada were enrolled. Bovine leukemia virus was most commonly earliest identified in pre-weaned heifers (18 herds, 32.1%) and the adult herd (18 herds, 32.1%). Ordinal logistic regression revealed that BLV was first identified in older age groups more often than in younger age groups when herds regrouped weaned heifers at least once, when fly control was used for breeding-age heifers, when herds practiced foot trimming on breeding-age heifers, and when bred heifers were brought in. Conclusion: Producers can use results to identify the youngest age group(s) in which BLV is identified and to tailor management strategies to prevent new infections.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.090
Threshold uncertainty score0.329

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 head0.011
GPT teacher head0.201
Teacher spread0.190 · 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

Citations1
Published2024
Admission routes3
Has abstractyes

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