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Record W1955921505 · doi:10.5210/ojphi.v7i1.5666

Surveillance to Manage Disease on Canadian Swine Farms

2015· article· en· W1955921505 on OpenAlexafffundabout
John Berezowski, Chris Byra, Egan Brockhoff, Dan Hurnik, Christian Klopfenstein, Harold Kloeze, L. E. Bergeron, George Charbonneau, Francois Cardinal, Iqbal Jamal, T. Herntier

Bibliographic record

VenueOnline Journal of Public Health Informatics · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Disease Management and Epidemiology
Canadian institutionsMinistère de l'Agriculture, des Pêcheries et de l'AlimentationCentre de Développement du Porc du QuébecCanadian Food Inspection AgencyUniversity of Prince Edward Island
FundersAgriculture and Agri-Food CanadaCanadian Swine Health Board
KeywordsMedical diagnosisDisease surveillanceCorporate governanceDiseaseData collectionData scienceBusinessKnowledge managementComputer scienceMedicinePathology

Abstract

fetched live from OpenAlex

The Canadian Swine Health Intelligence Network (CSHIN) was developed to help Canadian swine veterinarians and producers deal more effectively with swine disease. It consists of two integrated components; a social network and a web-based data collection, analysis and reporting system. The organizational structure engages the data and information providers directly in the decision making (governance) processes. The data collected include farm level syndrome prevalence as well as clinical and laboratory diagnoses. The CSHIN has demonstrated that it can provide value to both individual producers and the industry as a whole.

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)0.003
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.488
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 head0.184
GPT teacher head0.330
Teacher spread0.146 · 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 designNot applicable
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
Published2015
Admission routes3
Has abstractyes

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