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Record W4255592375 · doi:10.1109/cig.2019.8848025

CoG 2019 Program Committee

2019· article· en· W4255592375 on OpenAlexfundno aff

Bibliographic record

Venue2019 IEEE Conference on Games (CoG) · 2019
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
FundersSouthern University of Science and TechnologyUniversity of TsukubaRitsumeikan UniversityPontificia Universidade Católica do ParanáUniversity of CreteSejong UniversityUniversität Duisburg-EssenQueensland University of TechnologyUniversity of MelbourneRMIT UniversityArmy Research LaboratoryUniversity of TokyoUniversity of Nevada, RenoNational University of SingaporeÉcole Polytechnique Fédérale de LausanneTU Graz, Internationale Beziehungen und MobilitätsprogrammeYork UniversityGeorgia Institute of TechnologyKanazawa UniversityAustralian National UniversityKindai UniversityFlorida State UniversityUniversity of Colorado BoulderTurun YliopistoFordham UniversityMasarykova UniverzitaUniversity of Ontario Institute of TechnologyUniversity of AizuQueen Mary University of LondonUniversità ta' MaltaNational Chengchi UniversityTechnische Universität KaiserslauternTechnische Universiteit EindhovenUniversiteit LeidenDrexel UniversityNorth Carolina State UniversityUniversidade de LisboaUniversity of California, Santa CruzUniversity of Southern California
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 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)0.007
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.586
Threshold uncertainty score0.836

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0040.001
Scholarly communication0.0080.002
Open science0.0030.004
Research integrity0.0060.004
Insufficient payload (model declined to judge)0.4140.374

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 head0.068
GPT teacher head0.432
Teacher spread0.364 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

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".

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Citations0
Published2019
Admission routes1
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