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Record W1755593133

The Twenty-Eighth Veterans Administration—Armed Forces Pulmonary Disease Research Conference1

2015· article· en· W1755593133 on OpenAlexaboutno aff
Emil Rothstein

Bibliographic record

VenueAmerican Review of Respiratory Disease · 2015
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsAdministration (probate law)MedicineTuberculosisPulmonary tuberculosisPulmonary diseaseFamily medicineDiseaseUnit (ring theory)Operations researchGerontologyPolitical sciencePathologyLawPsychologyEngineeringInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

The 27th Veterans Administration-Armed Forces Pulmonary Disease Research Conference was held with the cooperation of the National Tuberculosis Association in Cincinnati, Ohio, from January 22 to 24, 1968. A previous report of thi8 conference appeared in the Review (1). At the 27th Conference there were 340 registrants including visitors and participants from Belgium, Canada, Great Britain, and India. One distinctive feature of these conferences is the cooperative studies. Plans and protocols for studies in tuberculosis and related mycobacterioses are discussed at the annual conference. All decisions for such research are democratically arrived at by the study unit representatives; after such a decision each hospital is free to participate in each study or to abstain. At each annual conference reports are made on continuing cooperative research projects as well as on other relevant material. Forty-six papers and several committee reports of current or proposed studies were presented. These are logically divided as follows:

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0060.004
Science and technology studies0.0010.001
Scholarly communication0.0050.002
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0170.005

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 head0.128
GPT teacher head0.447
Teacher spread0.318 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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 routes1
Has abstractyes

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