{"id":"W2026568547","doi":"10.1109/iv.2013.94","title":"Visual Clustering for Large Scale Commercial Enterprises","year":2013,"lang":"en","type":"article","venue":"","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Imperial Bank of Commerce (Canada)","funders":"","keywords":"Cluster analysis; Computer science; Interpretation (philosophy); Data mining; Scale (ratio); Representation (politics); Cluster (spacecraft); Data science; 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":[0.001603049,0.0005971708,0.000466822,0.002036193,0.00102914,0.002953851,0.001011863,0.0006344631,0.007932228],"category_scores_gemma":[0.005702207,0.0004145623,0.0006029124,0.002776336,0.0004612762,0.002268855,0.002669103,0.0008500707,0.001566557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000862884,"about_ca_system_score_gemma":0.0008822818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00544139,"about_ca_topic_score_gemma":0.007914376,"domain_scores_codex":[0.999106,0.0002784937,0.00007567698,0.0001438847,0.0003471153,0.00004891738],"domain_scores_gemma":[0.9977482,0.0008964856,0.0001485716,0.0005548336,0.0005279677,0.0001239564],"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":[0.0005616614,0.0002010576,0.006881963,0.0009899329,0.0001607358,0.001085297,0.006343255,0.08912691,0.03737229,0.1333551,0.08458479,0.6393369],"study_design_scores_gemma":[0.00008916677,0.00008554525,0.007754342,0.0002206078,0.00004532589,0.0006789427,0.002037042,0.6615112,0.01813041,0.1705577,0.1387574,0.0001323969],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03455343,0.0005357543,0.9328635,0.001386325,0.00006787657,0.0003007118,0.002182242,0.01865383,0.009456435],"genre_scores_gemma":[0.2061825,0.0005080111,0.7860916,0.0001332421,0.00003957997,0.0002445789,0.002556507,0.001573405,0.002670614],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007932228,"threshold_uncertainty_score":0.02653593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0213457743656423,"score_gpt":0.3194586458572124,"score_spread":0.2981128714915701,"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."}}