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Record W4390077597 · doi:10.1007/978-3-031-49333-1

Model and Data Engineering

2023· book· en· W4390077597 on OpenAlexfundno aff
Mohamed Mosbah, Tahar Kechadi, Ladjel Bellatreche, Faı̈ez Gargouri

Bibliographic record

VenueLecture notes in computer science · 2023
Typebook
Languageen
FieldComputer Science
TopicAdvanced Database Systems and Queries
Canadian institutionsnot available
FundersUniversidad de AlmeríaUniversité de SfaxUniversité de NantesShenzhen UniversityUniversità degli Studi di SalernoEidgenössisches NuklearsicherheitsinspektoratUniversité de BordeauxTartu ÜlikoolUniversità di BolognaNile UniversityOrta Doğu Teknik ÜniversitesiHunan UniversityUniversité de LorraineUniversity of MacedoniaUniverzita Karlova v PrazeUniversitat Politècnica de CatalunyaUniversity of CyprusUniversité Grenoble AlpesUniversidade do MinhoUniversité de MonctonUniversity of IoanninaUniversity of Ontario Institute of TechnologyUniversità degli Studi di MilanoIowa State UniversityIndian Institute of Technology, PatnaUniversity College DublinUniversidad de Oviedo
KeywordsComputer science

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)0.005
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 score0.019
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0050.006
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0190.010

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 head0.035
GPT teacher head0.271
Teacher spread0.236 · 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; 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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Citations0
Published2023
Admission routes1
Has abstractno

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