{"id":"W1537892751","doi":"10.1111/cgf.12644","title":"Detangler: Visual Analytics for Multiplex Networks","year":2015,"lang":"en","type":"article","venue":"Computer Graphics Forum","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Computer science; Multiplex; Cohesion (chemistry); Visual analytics; Feature (linguistics); Network analysis; Human–computer interaction; Data mining; Visualization; Distributed computing; Artificial intelligence","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":[0.001152132,0.001237465,0.0005762955,0.00284688,0.0004514186,0.002368882,0.001161106,0.0006420212,0.01423716],"category_scores_gemma":[0.005789554,0.0005438878,0.0006512549,0.001171796,0.0003956942,0.002575337,0.002982959,0.0008451585,0.001934645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005313987,"about_ca_system_score_gemma":0.0003171104,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002833802,"about_ca_topic_score_gemma":0.003638738,"domain_scores_codex":[0.999463,0.0001197349,0.0000364599,0.0001139489,0.0002243967,0.0000424368],"domain_scores_gemma":[0.9977549,0.001256242,0.0001578006,0.0003442866,0.0003268761,0.0001598674],"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":[0.002623023,0.0003570307,0.008526809,0.001548681,0.0002177909,0.001167892,0.004733704,0.04600281,0.09603182,0.03213606,0.06880596,0.7378484],"study_design_scores_gemma":[0.0002336289,0.0002113637,0.004646933,0.0002445866,0.00006863687,0.0004778081,0.001156602,0.8148602,0.05459213,0.036028,0.08733433,0.0001458079],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04476187,0.0004273138,0.8633918,0.0003147406,0.00006572153,0.0003111423,0.004358026,0.08154756,0.004821712],"genre_scores_gemma":[0.3309427,0.0004782754,0.6513606,0.0001677901,0.0000494231,0.000501607,0.005785479,0.00594094,0.004773233],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01423716,"threshold_uncertainty_score":0.04762805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04452672229331042,"score_gpt":0.3066135240822644,"score_spread":0.262086801788954,"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."}}