{"id":"W2792584195","doi":"10.1145/3181669","title":"A Visual Approach for Interactive Keyterm-Based Clustering","year":2018,"lang":"en","type":"article","venue":"ACM Transactions on Interactive Intelligent Systems","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cluster analysis; Computer science; Document clustering; Brown clustering; Flexibility (engineering); Correlation clustering; Task (project management); Relevance (law); Artificial intelligence; Information retrieval; Data mining; Canopy clustering algorithm; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003351343,0.0003620359,0.0003745237,0.0005584777,0.0003696276,0.0005386096,0.001360431,0.0001188925,0.00009171301],"category_scores_gemma":[0.0001452913,0.0003384241,0.0002638887,0.0005425708,0.0001122767,0.00096813,0.00004639977,0.000285258,0.0002194164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003249947,"about_ca_system_score_gemma":0.00009562256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008757638,"about_ca_topic_score_gemma":0.00002234521,"domain_scores_codex":[0.9975549,0.0001894123,0.0006352077,0.0008134256,0.0003776761,0.0004293946],"domain_scores_gemma":[0.9973707,0.0005321394,0.0002957512,0.0009982958,0.0006273015,0.0001757626],"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":[0.007126497,0.02114964,0.0004367439,0.002203773,0.004767129,0.00007489244,0.0407996,0.2884715,0.01737053,0.03060389,0.01476572,0.5722302],"study_design_scores_gemma":[0.0004802564,0.0009004751,0.00001157051,0.0002073235,0.00003545565,0.00002275182,0.001177696,0.9536832,0.02961375,0.00009916813,0.01338776,0.0003805951],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0004081551,0.0000135679,0.9943444,0.0001984636,0.002418416,0.0008706636,0.00008239361,0.0002818401,0.001382136],"genre_scores_gemma":[0.9642499,0.000006316877,0.03325575,0.0006078339,0.0002725406,0.0003843672,0.00005860193,0.00004531876,0.001119383],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9638417,"threshold_uncertainty_score":0.9999068,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04634535915117807,"score_gpt":0.3484522478146524,"score_spread":0.3021068886634743,"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."}}