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Machine Learning and Knowledge Discovery in Databases
Massih-Réza Amini, Stéphane Canu, Asja Fischer, Tias Guns, Petra Kralj Novak, Grigorios Tsoumakas
Bibliographic record
VenueLecture notes in computer science · 2023
Typebook
Languageen
FieldComputer Science
TopicData Mining Algorithms and Applications
Canadian institutionsnot available
FundersMicrosoft Research AsiaInstitut National de Recherche pour l'Agriculture, l'Alimentation et l'EnvironnementInsight SFI Research Centre for Data AnalyticsRégion NormandieStockholms UniversitetUniversity of Science and Technology of ChinaUniverza v LjubljaniUniversity of AlbertaKorea Advanced Institute of Science and TechnologyHarokopio UniversityScuola IMT Alti Studi LuccaUniversità di PisaCentral European Institute of TechnologyUniversidad de OviedoUniversité Grenoble AlpesUniversität BielefeldLudwig-Maximilians-Universität MünchenCollege of Engineering, Michigan State UniversityUniversity of WyomingDanmarks Tekniske UniversitetTemple UniversityUniversiteit UtrechtNankai UniversityCentre National de la Recherche ScientifiqueUniversiteit GentSapienza Università di RomaTechnische Universität WienUniversita degli Studi di Bari Aldo MoroUniversité de Caen NormandiePolitechnika PoznańskaMasdar Institute of Science and TechnologyMicrosoft ResearchInstitut National Polytechnique de ToulouseKing Abdulaziz UniversityNational and Kapodistrian University of AthensJohannes Gutenberg-Universität MainzLeibniz-GemeinschaftSimon Fraser UniversityUniversità degli Studi di PadovaEuskal Herriko UnibertsitateaAlbert-Ludwigs-Universität FreiburgDalhousie UniversityUniversity of WaikatoUniversidad de ZaragozaShanghai Educational Development FoundationChalmers Tekniska HögskolaRijksuniversiteit GroningenAalborg UniversitetAbu Dhabi National Oil CompanyUniversity of Texas at DallasZhejiang UniversityUniversité Paris-SaclayAalto-YliopistoSun Yat-sen UniversityTechnische Universität MünchenInstitut National des Sciences Appliquées de LyonUniversità degli Studi di Milano-BicoccaTsinghua UniversityChinese Academy of SciencesTU Graz, Internationale Beziehungen und MobilitätsprogrammeUniversity of MinnesotaMonash UniversityJulius-Maximilians-Universität WürzburgUniversity of Technology SydneyKU LeuvenUniversity of New South WalesGottfried Wilhelm Leibniz Universität HannoverAix-Marseille UniversitéUniversiteit LeidenUniversity of Texas at San AntonioIndian Council of Agricultural ResearchJilin UniversityNational University of SingaporeIndian Institute of Technology PalakkadCommonwealth Scientific and Industrial Research OrganisationUniversiti Kuala LumpurHelsingin YliopistoDeakin UniversityBlekinge Tekniska HögskolaBeihang UniversityUniversity of TwenteUniversity of BristolUniversity of OxfordSoutheast UniversityKungliga Tekniska HögskolanUlster UniversityUniversidade do PortoIndian Institute of Technology, PatnaCarl von Ossietzky Universität OldenburgItä-Suomen YliopistoShandong UniversityPolitechnika WarszawskaUniversité Bretagne SudUniversidad de CórdobaInstitut "Jožef Stefan"Université Catholique de LouvainPolitecnico di TorinoIndian National Science AcademyUniversità degli Studi di MilanoPurdue UniversityUniversità degli Studi di TrentoMichigan State UniversityUniversità degli Studi di CagliariGeorgia State UniversityDeutsches KrebsforschungszentrumUniversity of OttawaSyracuse UniversityMasarykova UniverzitaUniversiteit van AmsterdamAristotle University of ThessalonikiUniversity College Dublin
KeywordsComputer scienceKnowledge extractionDatabaseArtificial intelligenceInformation retrieval
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.014
Threshold uncertainty score0.048
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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 head0.024
GPT teacher head0.291
Teacher spread0.266 · 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods
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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Citations24
Published2023
Admission routes1
Has abstractno