Record W4229603319 · doi:10.1109/vtcfall.2019.8891398

VTC2019-Fall Technical Program Committee

2019· article· en· W4229603319 on OpenAlexfundno aff

Bibliographic record

Venuenot available

Typearticle

Languageen

FieldEngineering

TopicHeat Transfer and Numerical Methods

Canadian institutionsnot available

FundersUniversity of NicosiaAmerican University in CairoChiba UniversityNanjing UniversityIndian Institute of Technology DelhiWaterford Institute of TechnologyUniversitatea din BucureștiNational Institute of Standards and TechnologyNational Taipei University of TechnologySouth University of Science and Technology of ChinaNational Taiwan UniversityUniversidad Carlos III de MadridHong Kong University of Science and TechnologyHokkaido UniversityNational Chiao Tung UniversityNational Central UniversityNanjing University of Science and TechnologyNational and Kapodistrian University of AthensZhejiang UniversityIndian Statistical InstituteUniversitetet i OsloCOMSATS Institute of Information TechnologyTianjin UniversityWaseda UniversityNational Taiwan University of Science and TechnologyIndian Institute of Technology, PatnaKungliga Tekniska HögskolanGazi ÜniversitesiUniversität PaderbornSouthwest Jiaotong UniversityUniversity of South CarolinaQueen's UniversityBeijing Institute of TechnologyEast China Normal UniversityLunds UniversitetIstanbul Teknik ÜniversitesiConcordia UniversityShanghai Educational Development FoundationKorea UniversityInha UniversityXidian UniversityKyung Hee UniversitySoongsil UniversityNazarbayev UniversityNorthern Arizona UniversityPolitecnico di TorinoSeoul National UniversityUniversità degli Studi di PerugiaUniversity of New South WalesHarbin Institute of TechnologyLa Trobe UniversityMississippi State UniversityQueen's University BelfastUniversiti Tunku Abdul RahmanDurham UniversityInstituto Nacional de TelecomunicaçõesLoughborough UniversityHunan UniversityManchester Metropolitan UniversityUniversity of the West of ScotlandUniversité de LorraineUniversity of CyprusUniversitetet i AgderUniversitat Autònoma de BarcelonaNara Institute of Science and TechnologyInstitut national de recherche en informatique et en automatique (INRIA)King Abdullah University of Science and TechnologyIndian Institute of ScienceCairo UniversityMiddlesex UniversityTechnische Universiteit DelftQueen Mary University of LondonNational Cheng Kung UniversityUniversity of WaterlooKeio UniversityUniversity of OttawaZhejiang University of TechnologyNorth Carolina State UniversityGeorgia Institute of Technology

KeywordsComputer science

DOI10.1109/vtcfall.2019.8891398

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)0.013

metaresearch head score (Gemma)0.009

Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated

Candidate categoriesInsufficient payload (model declined to judge)

Consensus categoriesnone

DomainCandidate signal: none · Consensus signal: none

Study designCandidate signal: Not applicable · Consensus signal: Not applicable

GenreCandidate signal: Other · Consensus signal: Other

Teacher disagreement score0.840

Threshold uncertainty score0.535

Distilled classifier scores by category (both heads)

Category	Codex	Gemma
Metaresearch	0.013	0.009
Meta-epidemiology (narrow)	0.002	0.001
Meta-epidemiology (broad)	0.001	0.001
Bibliometrics	0.003	0.002
Science and technology studies	0.003	0.001
Scholarly communication	0.006	0.002
Open science	0.003	0.004
Research integrity	0.004	0.003
Insufficient payload (model declined to judge)	0.160	0.189

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 head0.012

GPT teacher head0.277

Teacher spread0.265 · 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