{"id":"W2495532368","doi":"10.4018/978-1-59140-941-1.ch008","title":"Modeling Relevance Relations Using Machine Learning Techniques","year":2007,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Software Engineering Research","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Relevance (law); Computer science; Artificial intelligence; Relation (database); Machine learning; Software; Software deployment; Abstraction; Precision and recall; Data mining; Data science; Software engineering; Programming language","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005644228,0.001487453,0.001592793,0.00641526,0.001308961,0.003680916,0.002730595,0.001949847,0.003742729],"category_scores_gemma":[0.0291829,0.0009694051,0.001981672,0.00519699,0.00130748,0.008053298,0.00196298,0.003160332,0.001561721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002223287,"about_ca_system_score_gemma":0.001480797,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006202288,"about_ca_topic_score_gemma":0.00653259,"domain_scores_codex":[0.9939017,0.002333607,0.000413069,0.001169104,0.001890665,0.0002919134],"domain_scores_gemma":[0.9797848,0.01679466,0.00111005,0.001048924,0.00110121,0.0001603984],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001619678,0.0003723098,0.0107855,0.0006556456,0.0002707426,0.0008650306,0.001123018,0.4063152,0.002264223,0.1588385,0.01596707,0.4023808],"study_design_scores_gemma":[0.00001328001,0.00002548768,0.0009460145,0.00007548531,0.0000378346,0.0001942629,0.00007297277,0.839862,0.0006457201,0.1516599,0.006442645,0.00002439014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01808096,0.002882139,0.9706194,0.001171847,0.00008761106,0.0001830382,0.0004803225,0.001013813,0.005480848],"genre_scores_gemma":[0.3614319,0.003069554,0.6257532,0.0004666364,0.00043571,0.0005729354,0.002051138,0.0003342221,0.005884731],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00641526,"threshold_uncertainty_score":0.02984989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04374587496380003,"score_gpt":0.2999586025762956,"score_spread":0.2562127276124956,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}