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Machine Learning and Knowledge Discovery in Databases
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 · 2021
Typebook
Languageen
FieldComputer Science
TopicData Mining Algorithms and Applications
Canadian institutionsnot available
FundersInstitute of Computing Technology, Chinese Academy of SciencesTélécom ParisUniversity of California, DavisÉcole Normale Supérieure de LyonUniversity of Illinois at Urbana-ChampaignLG ElectronicsTechnische Universität DortmundSabancı ÜniversitesiITMO UniversityUniversity of Chinese Academy of SciencesRyukoku UniversityUniversität KonstanzEötvös Loránd TudományegyetemStockholms UniversitetXiamen UniversityUniversità della CalabriaSingapore University of Technology and DesignTechnische Universität BerlinIndian Institute of Technology GuwahatiUniversidade do PortoSamsungUniversidade Estadual de CampinasNanjing UniversityIndian Institute of Technology BombayUniversità degli Studi dell'AquilaLudwig-Maximilians-Universität MünchenShanghai Jiao Tong UniversityRheinische Friedrich-Wilhelms-Universität BonnVrije Universiteit BrusselWestfälische Wilhelms-Universität MünsterShanghai University of Finance and EconomicsUniversidade Federal de UberlândiaCentre National de la Recherche ScientifiqueUniversidade de MacauDartmouth CollegeUniversity of TokyoUniversity of RochesterZhengzhou UniversityInstitut "Jožef Stefan"Waseda UniversityUniversiteit GentSorbonne UniversitéKungliga Tekniska HögskolanUniversity of WarwickIndian Institute of Technology MadrasJohns Hopkins UniversityHarbin Institute of TechnologyUniversité de NamurInstitut National de Recherche pour l'Agriculture, l'Alimentation et l'EnvironnementUniversität WienHeinrich-Heine-Universität DüsseldorfEidgenössische Technische Hochschule ZürichNational Council for Scientific ResearchUniversity of Texas at DallasZhejiang UniversityAalto-YliopistoUniversità di PisaUniversidad Autónoma de MadridPohang University of Science and TechnologyBeijing Institute of TechnologyFudan UniversitySun Yat-sen UniversityUniversity of TwenteChongqing UniversityInsight SFI Research Centre for Data AnalyticsBeijing Jiaotong UniversityTechnische Universiteit DelftUniversity of TorontoUniversity of Electronic Science and Technology of ChinaTechnische Universität KaiserslauternMonash UniversityPeking UniversityKU LeuvenUniversity of New South WalesGottfried Wilhelm Leibniz Universität HannoverRijksuniversiteit GroningenItä-Suomen YliopistoEberhard Karls Universität TübingenUniversiteit LeidenUmeå UniversitetHelsingin YliopistoUniversidade Federal do ParanáDeakin UniversityUniversity of BristolUniversidade Federal do Rio Grande do SulTechnische Universität MünchenJoint Research CentreUniversité d'OrléansInstitut National des Sciences Appliquées de LyonTsinghua UniversityChinese Academy of SciencesIndian National Science AcademyUniversity of ExeterUniversity of East AngliaUniversity of AdelaideUniversité Grenoble AlpesUniversitat Politècnica de CatalunyaIndian Institute of Technology KanpurUniversity of WaikatoFlorida State UniversityUniversidade de CoimbraOregon State UniversityUniversity of QueenslandEmory UniversityPolytechnique MontréalUniversidad de AlicanteLeuphana Universität LüneburgToyota Research InstitutePolitecnico di TorinoUniversität KasselCase Western Reserve UniversityInstitut National Polytechnique de ToulouseMcGill UniversityUniversité de LorraineUniversité du LuxembourgPhilipps-Universität MarburgLeibniz-GemeinschaftPennsylvania State UniversityUniversity of OxfordTianjin UniversityUniversität zu LübeckUniversiteit UtrechtSiemensAarhus UniversitetHarvard UniversityUniversité de LiègeEuropean CommissionUniversity of AlbertaUniversity of Science and Technology of ChinaChongqing University of Posts and TelecommunicationsWashington State UniversityUniversity College DublinTechnische Universiteit EindhovenArizona State UniversityUniversity of TsukubaPurdue UniversityUniversity of Southern California
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.
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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.991
Threshold uncertainty score1.000
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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 head0.019
GPT teacher head0.281
Teacher spread0.262 · 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
Citations5
Published2021
Admission routes1
Has abstractno