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About Metrics for Clone Detection

2014· article· en· W2295126561 on OpenAlexaff
Thierry Lavoie, Ettore Merlo

Bibliographic record

VenuePolyPublie (École Polytechnique de Montréal) · 2014
Typearticle
Languageen
FieldComputer Science
TopicWeb Data Mining and Analysis
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsSimilarity (geometry)clone (Java method)Computer scienceCosine similarityScalabilityFunction (biology)DetectorData miningTrigonometric functionsQuality (philosophy)Artificial intelligencePattern recognition (psychology)MathematicsTelecommunicationsGeneDatabase

Abstract

fetched live from OpenAlex

This paper presents some results about some metrics and their possible impact on clone detectors.It also discusses some advantages of why we should use metrics instead of arbitrary measures.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.028
metaresearch head score (Gemma)0.211
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 score0.028
Threshold uncertainty score0.147

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.211
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0150.019
Science and technology studies0.0030.005
Scholarly communication0.0090.030
Open science0.0030.004
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0050.002

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 head0.012
GPT teacher head0.231
Teacher spread0.220 · 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".

Quick stats

Citations1
Published2014
Admission routes1
Has abstractyes

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