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Record W2608080273 · doi:10.3168/jds.2016-12501

Factors associated with dairy calf health in automated feeding systems in the Upper Midwest United States

2017· article· en· W2608080273 on OpenAlexaff
Matthew W. Jorgensen, Amber Adams‐Progar, A.M. de Passillé, J. Rushen, S. Godden, H. Chester-Jones, M.I. Endres

Bibliographic record

VenueJournal of Dairy Science · 2017
Typearticle
Languageen
FieldVeterinary
TopicAnimal health and immunology
Canadian institutionsUniversity of British Columbia
FundersNational Institute of Food and Agriculture
KeywordsOddsOdds ratioDiarrheaMedicineAllowance (engineering)Milk feverAnimal scienceVeterinary medicineDemographyLogistic regressionBiologyLactationPregnancyInternal medicineIce calving

Abstract

fetched live from OpenAlex

/calf) was associated with higher ear and eye scores, whereas larger group sizes were associated with increased odds of higher nasal score and decreased odds of higher hide dirtiness score. Rectangular pen shape was associated with decreased odds of higher eye score. Absence of a positive pressure ventilation tube was associated with increased odds of having a calf detected with a fever. Based on these results, we hypothesize that these factors could be managed to improve health outcomes for dairy calves on automated feeding systems.

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)0.004
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 score0.009
Threshold uncertainty score0.580

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.120
GPT teacher head0.383
Teacher spread0.263 · 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

Citations68
Published2017
Admission routes1
Has abstractyes

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