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Record W4220872743 · doi:10.3168/jdsc.2022-3-2-167

Erratum to “Risk factors for morbidity in 1- to 9-day-old dairy calves following caustic paste disbudding” (JDS Commun. 2:376–380)

2022· erratum· en· W4220872743 on OpenAlexaboutno aff
Cassandra N. Reedman, T.F. Duffield, T.J. DeVries, K. Lissemore, Charlotte B. Winder

Bibliographic record

VenueJDS Communications · 2022
Typeerratum
Languageen
FieldVeterinary
TopicAnimal health and immunology
Canadian institutionsnot available
Fundersnot available
KeywordsCaustic (mathematics)Animal scienceMedicineDemographyMathematicsBiologySociologyGeometry

Abstract

fetched live from OpenAlex

The animal ethics statement was missing from this paper. The following statement should be included: “Use of animals and all methods for this study were approved by the University of Guelph Animal Care Committee (AUP#4001) in compliance with animal use guidelines of the Canadian Council on Animal Care (1993).” Risk factors for morbidity in 1- to 9-day-old dairy calves following caustic paste disbuddingJDS CommunicationsVol. 2Issue 6PreviewCalfhood disease has been linked to decreased growth, decreased survival to first calving, and increased age at first calving, and it adversely affects first-lactation milk production (Stanton et al., 2012). Prevalence of calfhood diseases is variable across dairy operations, ranging from 13.7 to 37.2% (NAHMS, 2011; Calderón-Amor and Gallo, 2020). There are many ways to mitigate the risk of adverse health events in calves, including proper colostrum management and reducing environmental stress or stressful events (Beam et al., 2009; Urie et al., 2018; Renaud et al., 2020). Full-Text PDF Open Access

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.040
Threshold uncertainty score0.135

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0400.026

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 head0.138
GPT teacher head0.406
Teacher spread0.268 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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 routes1
Has abstractyes

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