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Record W2995494766

The Run of Depression Resilience Phenotype from a Natural Disease Challenge in Pigs

2019· article· en· W2995494766 on OpenAlexaff
Jack C. M. Dekkers, Austin M. Putz, John C. S. Harding, Graham Plastow, PigGen Canada, Michael K. Dyck, Frédéric Fortin

Bibliographic record

VenueIowa State University animal industry report · 2019
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsCentre de Développement du Porc du QuébecUniversity of AlbertaUniversity of Saskatchewan
Fundersnot available
KeywordsPhenotypeResilience (materials science)LivestockDepression (economics)Psychological resilienceBiologyDiseaseAgricultureMedicinePsychologyGeneticsInternal medicineEcologyGeneEconomicsSocial psychology
DOInot available

Abstract

fetched live from OpenAlex

Resilience phenotypes will be needed in the future as livestock industries adopt precision agriculture technology on a large portion of commercial animals. The current study introduces a run of depression (ROD) phenotype to quantify resilience using individual daily feed intake data. This phenotype was lowly heritable (0.11±0.07) but was genetically correlated to mortality (0.75-0.98) and treatment rate (0.61-0.84).

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 score0.013
Threshold uncertainty score0.549

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 integrity0.0000.001
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 head0.034
GPT teacher head0.286
Teacher spread0.252 · 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
Published2019
Admission routes1
Has abstractyes

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