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Record W1970322322 · doi:10.12927/hcq.2011.22641

Influenza Immunization Data: Can We Make Order Out of Chaos?

2011· article· en· W1970322322 on OpenAlexaff
Jennifer Pereira, Christine Heidebrecht, Susan Quach, Sherman Quan, Michael Finkelstein, Julie A. Bettinger, Shelley L. Deeks, Maryse Guay, David L. Buckeridge, Larry W. Chambers, Natasha S. Crowcroft, Beate Sander, Donna Kalailieff, Stephanie Brien, Jeffrey C. Kwong

Bibliographic record

VenueHealthcare Quarterly · 2011
Typearticle
Languageen
FieldMedicine
TopicData-Driven Disease Surveillance
Canadian institutionsPublic Health Ontario
Fundersnot available
KeywordsImmunizationCHAOS (operating system)Order (exchange)Best practiceMedicineVirologyComputer scienceBusinessPolitical scienceImmunologyComputer securityImmune systemLaw

Abstract

fetched live from OpenAlex

he 2009 H1N1 pandemic vaccination campaign was an excellent opportunity to witness the many benefits of collecting individual-level immunization data at the point of vaccination. Most provinces and territories required the reporting of at least partial demographic vaccination data from their local public health agencies, so timely vaccine coverage data were available to inform operational planning and infection prevention activities.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.669
Threshold uncertainty score0.688

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 head0.090
GPT teacher head0.350
Teacher spread0.260 · 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

Citations3
Published2011
Admission routes1
Has abstractyes

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