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CBDCom 2015 Organizing and Program Committees

2015· article· en· W4255174050 on OpenAlexfundno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicMedical Coding and Health Information
Canadian institutionsnot available
FundersInstitute for Infocomm ResearchMahanakorn University of TechnologyUniversidade Federal de Mato Grosso do SulNanchang Institute of TechnologyConservatoire National des Arts et MétiersUniversity of Science and Technology of ChinaShenzhen UniversityNational University of Defense TechnologyUppsala UniversitetWuhan UniversityZhejiang UniversityUniversidade de São PauloRMIT UniversityTsinghua UniversityBeijing University of TechnologyNational Institute of Standards and TechnologySwinburne University of TechnologyUniversidade Estadual de Ponta GrossaFudan UniversityUniversity of California, Santa BarbaraOxford Brookes UniversityDalian University of TechnologyUniversiti MalayaConcordia UniversityLa Trobe UniversityChinese Academy of SciencesTianjin UniversityUniverza v LjubljaniUniversity of WollongongChina University of Petroleum, BeijingBeihang UniversityLunds UniversitetUniversidade Lusófona de Humanidades e TecnologiasFordham UniversityShandong UniversityJinan UniversityUniversity of LeicesterMicrosoft Research
KeywordsComputer science

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.001
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.591
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0000.001

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 head0.482
GPT teacher head0.554
Teacher spread0.072 · 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".

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Citations0
Published2015
Admission routes1
Has abstractno

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