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SPAWC 2022 Cover Page

2022· article· en· W4288388726 on OpenAlexfundno aff

Bibliographic record

Venue2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC) · 2022
Typearticle
Languageen
FieldDecision Sciences
TopicAcademic Publishing and Open Access
Canadian institutionsnot available
FundersU.S. Army Combat Capabilities Development CommandNational Technical University of AthensArmy Research LaboratoryNazarbayev UniversitySoutheast UniversitySingapore University of Technology and DesignTechnische Universität BerlinInstitute for Infocomm ResearchNational Tsing Hua UniversityTechnische Universität BraunschweigUniversidad de CantabriaTechnische Universität WienKungliga Tekniska HögskolanTechnische Universiteit DelftQueen's UniversityKeio Universityİslam Tarih, Sanat ve Kültür Araştırma MerkeziUniversitat Pompeu FabraUniversità degli Studi di PerugiaUniversität SiegenNew Mexico State UniversityNorges Teknisk-Naturvitenskapelige UniversitetTemple UniversityUniversidad de GranadaNational and Kapodistrian University of AthensChinese Academy of SciencesAthens University of Economics and BusinessNorth Carolina State UniversityUniversité du LuxembourgUniversity of CyprusLinköpings UniversitetQueen's University BelfastShanghai Advanced Research Institute, Chinese Academy of SciencesUniversity of New South WalesUniversity of AlbertaTechnische Universität MünchenQualcommIndian Institute of ScienceUniversity of MinnesotaBeihang UniversityDEVCOM Army Research LaboratoryUniversity of Pennsylvania
KeywordsCover (algebra)Computer scienceInformation retrievalEngineering

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)0.001
metaresearch head score (Gemma)0.001
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 score0.262
Threshold uncertainty score0.373

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.7380.749

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 head0.055
GPT teacher head0.393
Teacher spread0.338 · 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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Published2022
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