{"id":"W2513020503","doi":"","title":"Combining relevance feedback and genetic algorithms in an internet information filtering engine","year":2000,"lang":"en","type":"article","venue":"RIAO Conference","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Relevance (law); Computer science; The Internet; Humanities; Algorithm; Philosophy; Political science; World Wide Web","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":[0.003130565,0.000826686,0.001248359,0.002207475,0.0007647947,0.001537793,0.001366476,0.001761091,0.001090189],"category_scores_gemma":[0.00634231,0.0005181373,0.0007057609,0.001486436,0.0006111646,0.001748926,0.0006975304,0.0007322051,0.0004780261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001278366,"about_ca_system_score_gemma":0.001340303,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01351781,"about_ca_topic_score_gemma":0.01610186,"domain_scores_codex":[0.9989089,0.0003356022,0.00007083418,0.0002002532,0.0003598447,0.000124508],"domain_scores_gemma":[0.9975127,0.00159047,0.0001155106,0.0001441528,0.0005556684,0.00008150005],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004106082,0.0004067167,0.004405046,0.0001723989,0.0002232314,0.0002412984,0.0003325434,0.2952668,0.01632687,0.004877962,0.001554326,0.6757823],"study_design_scores_gemma":[0.00008388633,0.0002137107,0.0011314,0.00002234228,0.0001197474,0.0001126696,0.00006183113,0.983869,0.0084531,0.00379736,0.002102978,0.00003200881],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2101184,0.001543558,0.777078,0.0007227185,0.0001623359,0.0002949421,0.00005879704,0.003865365,0.006155865],"genre_scores_gemma":[0.5492257,0.000407335,0.4444838,0.0002590613,0.0001029401,0.0001581077,0.00009399544,0.0001364555,0.005132514],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01351781,"threshold_uncertainty_score":0.02687824,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02146250515112161,"score_gpt":0.2408482602044399,"score_spread":0.2193857550533183,"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."}}