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Record W3117391841 · doi:10.21423/aabppro20183218

Canadian National Dairy Study

2018· article· en· W3117391841 on OpenAlexaffabout
Charlotte B. Winder, C.A. Bauman, T.F. Duffield, Herman W. Barkema, Greg Keefe, J. Dubuc, Fabienne D. Uehlinger, D.F. Kelton

Bibliographic record

VenueAmerican Association of Bovine Practitioners Conference Proceedings · 2018
Typearticle
Languageen
FieldVeterinary
TopicAnimal health and immunology
Canadian institutionsUniversity of Prince Edward IslandUniversity of SaskatchewanUniversité de MontréalUniversity of CalgaryUniversity of Guelph
Fundersnot available
KeywordsWelfareAnimal welfareMilk productionAnimal husbandryEnvironmental healthSocioeconomicsAnimal scienceMedicineGeographyBiologyPolitical scienceEconomicsAgriculture

Abstract

fetched live from OpenAlex

Care and management of dairy heifer calves early in life has substantial short- and long-term impacts, from affecting calf morbidity and mortality rates to future milk production. In the past decade, substantial changes have occurred in the way dairy heifer calves are managed. Animal welfare standards have also changed globally. While there is some evidence that the use of pain control for disbudding has increased in the province of Ontario in recent years, no national data have been collected regarding this practice. Other early life heifer calf management practices, as well as morbidity and mortality rates, have not been described at the Canadian national level. In this regard, the objectives of this study, part of phase I of the 2015 Canadian National Dairy Study, were to examine heifer calf health, adoption of rearing practices, and explore factors associated with different rearing strategies on Canadian dairy farms.

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)0.001
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 score0.073
Threshold uncertainty score0.936

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.344
Teacher spread0.305 · 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
Published2018
Admission routes2
Has abstractyes

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