{"id":"W1481682349","doi":"10.1007/11425274_58","title":"A Distance-Based Algorithm for Clustering Database User Sessions","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Cluster analysis; Benchmark (surveying); Computation; Workload; Data mining; Online transaction processing; Database; Algorithm; Database transaction; Artificial intelligence; Transaction processing","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0008353093,0.0006069973,0.0005093048,0.0008051607,0.0004224417,0.001053704,0.004675632,0.0001926109,0.00002842309],"category_scores_gemma":[0.00004447299,0.0005522752,0.0001750784,0.0005784413,0.0004052942,0.001570154,0.002270637,0.0005296182,0.00003510596],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002775487,"about_ca_system_score_gemma":0.0004093497,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001205089,"about_ca_topic_score_gemma":0.0001216141,"domain_scores_codex":[0.9957287,0.00002178641,0.0005355953,0.001920821,0.0009289919,0.0008640903],"domain_scores_gemma":[0.996784,0.0004421713,0.0002813979,0.002088175,0.0001832187,0.0002210279],"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":[0.000003028276,0.00003462652,0.000002810924,0.00004255417,0.000008616057,0.00004331022,0.00005434656,0.006881614,0.00001281081,0.00596781,0.0002138427,0.9867346],"study_design_scores_gemma":[0.0005132793,0.00008376226,0.000006784153,0.0004634477,0.000012848,0.000008212218,1.03673e-7,0.9208423,0.0002313806,0.007793997,0.06937793,0.0006659235],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[4.891439e-7,0.0003172658,0.9944043,0.001023064,0.002036544,0.0007891562,0.0001394885,0.0002487718,0.001040978],"genre_scores_gemma":[0.0002703411,0.00004064423,0.9949694,0.002087962,0.0008892194,0.00004690567,0.0001113017,0.00004594757,0.001538244],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9860687,"threshold_uncertainty_score":0.9999833,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0233684434842335,"score_gpt":0.2653069685680108,"score_spread":0.2419385250837773,"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."}}