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Artificial Neural Networks and Machine Learning – ICANN 2023
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.
Bibliographic record
VenueLecture notes in computer science · 2023
Typebook
Languageen
FieldComputer Science
TopicMachine Learning in Healthcare
Canadian institutionsnot available
FundersUniversity of North Carolina WilmingtonTélécom ParisUniversidad Autónoma de TamaulipasShanghai University of Electric PowerUniversité de Franche-ComtéUniversitat Politècnica de CatalunyaHefei University of TechnologySouthern University of Science and TechnologyHefei UniversityUniversity of MoratuwaIstituto Italiano di TecnologiaHandong Global UniversityHarokopio UniversityMount Kenya UniversityUniversidade do MinhoRheinische Friedrich-Wilhelms-Universität BonnNanjing UniversitySoochow UniversityIstanbul Teknik ÜniversitesiUniversity of TsukubaAristotle University of ThessalonikiUniversità Cattolica del Sacro CuoreUniversität HamburgUniversity of Electronic Science and Technology of ChinaUniversitetet i OsloLeibniz-GemeinschaftNanjing University of Science and TechnologyShanghai Jiao Tong UniversityAkademie Věd České RepublikyTechnische Universität BerlinUniversity of BristolUniversité de StrasbourgNational and Kapodistrian University of AthensChinese Academy of SciencesYork UniversityUniversity of BrightonUniversiteit LeidenEberhard Karls Universität TübingenIowa State UniversityUlster UniversityArizona State UniversityTechnische Universiteit EindhovenUniversity of the West of EnglandShenzhen UniversityUniversity of LeicesterNorthwestern UniversityHamad Bin Khalifa UniversityUniversitat Jaume ITongji UniversityPolitechnika PoznańskaBaiduUniversité du Littoral Côte d'OpaleKhalifa University of Science, Technology and ResearchUniverzita Komenského v BratislaveFriedrich-Schiller-Universität JenaUniversity of CyprusUniversità degli Studi di SienaDeutsches Forschungszentrum für Künstliche IntelligenzCentral South UniversityKyungpook National UniversityKU LeuvenNanjing Normal UniversityUniversity of Nevada, RenoUniversity of Southern CaliforniaUniversity of EssexNorthwestern Polytechnical UniversityKing's College LondonEidgenössisches NuklearsicherheitsinspektoratČeské Vysoké Učení Technické v PrazeAcadia UniversityUniversity of DundeeUniversité de BordeauxNorthwest Normal UniversityRitsumeikan UniversityBaylor UniversityGuilin University of Electronic Technology
KeywordsComputer scienceArtificial neural networkArtificial intelligenceMachine learning
Abstract
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.
Full frame distilled prediction
Teacher imitationNot 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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.628
Threshold uncertainty score1.000
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 head0.025
GPT teacher head0.279
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itQuick stats
Citations29
Published2023
Admission routes1
Has abstractno