{"id":"W2125811916","doi":"10.1109/icsmc.2007.4414079","title":"Learning social networks using multiple resampling method","year":2007,"lang":"en","type":"article","venue":"","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Undersampling; Computer science; Resampling; Oversampling; Artificial intelligence; Relation (database); Classifier (UML); Machine learning; Support vector machine; Class (philosophy); Social network (sociolinguistics); Set (abstract data type); Data mining; Social media; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008362278,0.00007749194,0.00008863379,0.0001111489,0.0003508061,0.000142116,0.000453919,0.00009452289,0.00001168979],"category_scores_gemma":[0.0001095988,0.00007142084,0.00004292396,0.0004212165,0.00002722608,0.0003003514,0.0002333287,0.0001999099,0.000009315901],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004493909,"about_ca_system_score_gemma":0.00001351865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000300276,"about_ca_topic_score_gemma":0.000007886698,"domain_scores_codex":[0.9991347,0.0000330796,0.0001804394,0.0002374552,0.0001364448,0.0002778984],"domain_scores_gemma":[0.9994072,0.0002504076,0.00008522656,0.0001845866,0.00004332547,0.00002921539],"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":[0.000004927862,0.00001974727,0.005460729,0.000002538808,0.00001001573,0.000003552439,0.0004532319,0.00747871,0.00499656,0.1938795,0.0002159474,0.7874745],"study_design_scores_gemma":[0.0001536256,0.00001661331,0.002760295,0.000003076329,0.000002265533,0.000003597757,0.0004537565,0.9829696,0.005157877,0.002580153,0.00575473,0.0001443613],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007517304,0.00002864007,0.9888076,0.0003456671,0.0001421529,0.00005427054,3.286041e-8,0.001021745,0.002082617],"genre_scores_gemma":[0.552707,0.000001521321,0.4469604,0.00005834939,0.00004895682,8.912929e-7,3.948993e-7,0.000003462892,0.0002190466],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9754909,"threshold_uncertainty_score":0.2912457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05936525729498046,"score_gpt":0.3507767445331882,"score_spread":0.2914114872382078,"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."}}