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Record W3195819010 · doi:10.21423/aabppro20044977

Relationship Between Disease Occurrence, Feeding Management and Return Over Feed in Ontario Dairy Herds

2004· article· en· W3195819010 on OpenAlexaffabout
C.J. McLaren, K. Lissemore, K. Leslie, T.F. Duffield, D.F. Kelton, B. Grexton

Bibliographic record

VenueAmerican Association of Bovine Practitioners Conference Proceedings · 2004
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsHerdLamenessRetained placentaMastitisCullingKetosisSomatic cell countMedicineIncidence (geometry)DiseaseAnimal scienceVeterinary medicineLactationBiologyInternal medicineIce calvingPregnancySurgeryEndocrinology

Abstract

fetched live from OpenAlex

On a worldwide basis, dairy industries of most countries are becoming increasingly concerned with factors that impact return on investment. Historically, research has focused on the relationship between management, production-limiting disease and milk production. However, few studies have quantified their association with herd economics.
 The objectives of this research are to examine the relationship between profitability as measured by the Ontario Dairy Herd Improvement (DHI) Corporation's Return over Feed (ROF) index, and herd characteristics such as milk production, somatic cell count linear score and health management practices. The ROF index will also be used to assess on-farm health and disease information that includes lameness, clinical ketosis, clinical mastitis, retained placenta (RP), displaced abomasum (DA), milk fever, monensin use and ration particle size. Herd level incidence risks for subclinical ketosis and subclinical mastitis will be evaluated by using the California Mastitis Test and the KetoTestO ketone test in early postpartum cows. Subsequently, the associations between the risk of disease, management factors and ROF will be determined.

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 score0.005
Threshold uncertainty score0.642

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 head0.016
GPT teacher head0.254
Teacher spread0.238 · 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
Published2004
Admission routes2
Has abstractyes

Explore more

Same venueAmerican Association of Bovine Practitioners Conference ProceedingsSame topicGenetic and phenotypic traits in livestockFrench-language works237,207