{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008620266,0.00006249497,0.00007794515,0.00004525692,0.00009577238,0.000275017,0.0003769427,0.00002254997,0.0001956221],"category_scores_gemma":[0.0000163256,0.00005352388,0.00003939385,0.0001059597,0.000008018653,0.0005247226,0.0002597952,0.00002304399,0.0002038716],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008575126,"about_ca_system_score_gemma":0.00001248307,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001772621,"about_ca_topic_score_gemma":0.00004866625,"domain_scores_codex":[0.9994267,0.00001263316,0.000128437,0.0001551045,0.00009422272,0.0001828504],"domain_scores_gemma":[0.9996344,0.00003349844,0.00002809409,0.0001748061,0.00006375089,0.00006548993],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001282376,0.001236623,0.01982863,0.0001393098,0.00006997904,0.000002536525,0.003051696,0.0001019435,0.001537914,0.2321836,0.5760314,0.1658036],"study_design_scores_gemma":[0.0003267202,0.00004269299,0.001581113,0.000006262823,0.00000225106,0.000001062831,0.00007245273,0.9536715,0.0005234304,0.000366532,0.04329935,0.0001066648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002508425,0.000003844946,0.9940208,0.0006459354,0.0001840772,0.000136123,0.00000585633,0.0001224763,0.002372439],"genre_scores_gemma":[0.8949638,0.000006573604,0.09385866,0.006655365,0.0001600908,0.00003915188,0.00004265217,0.00001234155,0.004261349],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9535695,"threshold_uncertainty_score":0.2651995,"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."}}