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Record W4281688450 · doi:10.3389/fvets.2022.939744

Corrigendum: Association Between Recycled Manure Solids Bedding and Subclinical Mastitis Incidence: A Canadian Cohort Study

2022· erratum· en· W4281688450 on OpenAlexaffabout
Annie Fréchette, Gilles Fecteau, Caroline Côté, Simon Dufour

Bibliographic record

VenueFrontiers in Veterinary Science · 2022
Typeerratum
Languageen
FieldAgricultural and Biological Sciences
TopicFood Waste Reduction and Sustainability
Canadian institutionsInstitut de Recherche et de Développement en AgroenvironnementFonds de Recherche du Québec – Nature et TechnologiesUniversité de Montréal
Fundersnot available
KeywordsIncidence (geometry)Subclinical infectionCohortManureMedicineMastitisBeddingCohort studyAssociation (psychology)Animal scienceEnvironmental healthVeterinary medicineInternal medicineBiologyAgronomyPsychologyMathematicsPathology

Abstract

fetched live from OpenAlex

In the original article, there were mistakes in Tables 2, 3 and 4 as published. Data alignment problems were present in these tables. The corrected Tables 2, 3 and 4 appears below. The authors apologize for these errors and state that this does not change the scientific conclusions of the article in any way. The original article has been updated. herd size were centered on 5 years and 100 cows, respectively. The intercept, therefore, represents the cows' mean LS for a cow in a 100 milking cows herd that had renovated its stalls 5 years ago. ‡ Coefficient represent an increase of 10 years.⁑ Coefficient represent an increase of 100 cows.⁂ Putative confounders. Farm 0.10 § Confidence interval of the incidence ratio (IR). † Stall age and herd size were centered on 5 years and 100 cows, respectively. The intercept, therefore, represents the cow's log risk of having a linear score > 4.0 for a cow in a 100 milking cow herd that had renovated its stalls 5 years ago. ‡ Coefficient represent an increase of 10 years.⁑ Coefficient represent an increase of 100 cows.⁂ Putative confounders.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.041
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.025
GPT teacher head0.271
Teacher spread0.246 · 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
Published2022
Admission routes2
Has abstractyes

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