{"id":"W2927658665","doi":"10.1007/11970125_10","title":"A Randomized Algorithm for Online Unit Clustering","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Optimization and Search Problems","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Competitive analysis; Computer science; Online algorithm; Partition (number theory); Cluster analysis; Randomized algorithm; Upper and lower bounds; Extension (predicate logic); Algorithm; Set (abstract data type); Partition problem; Combinatorics; Mathematics; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003043564,0.0004709957,0.0008314825,0.001171304,0.0002612473,0.0005661833,0.002762608,0.0003122001,0.00002407325],"category_scores_gemma":[0.0002049347,0.0004088551,0.000245338,0.0006271122,0.0007143156,0.0004826149,0.001146195,0.0006472978,0.00001514341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001633437,"about_ca_system_score_gemma":0.0004899226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001593005,"about_ca_topic_score_gemma":0.00009163296,"domain_scores_codex":[0.9963089,0.00005948898,0.0007154607,0.00125129,0.0008854843,0.000779341],"domain_scores_gemma":[0.9965104,0.001487888,0.0002898688,0.0009603308,0.0005209464,0.0002305132],"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":[0.0001350043,0.00002547215,3.26683e-7,0.00002744247,0.00001324661,0.00001885936,0.0002686057,0.05471187,0.000003915967,0.008402621,0.000007726537,0.9363849],"study_design_scores_gemma":[0.01557864,0.00009123801,8.453327e-7,0.0002108815,0.000006819658,0.00002898874,7.950408e-8,0.9249141,0.00006360791,0.05566439,0.00300298,0.0004374261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[9.774874e-7,0.0002470288,0.9940131,0.0006945938,0.001359467,0.001381771,0.00001405712,0.0002030901,0.002085885],"genre_scores_gemma":[0.0001623737,0.00008796361,0.9958948,0.002040908,0.0004257916,0.0000237328,0.00002345874,0.00003667675,0.001304304],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9359475,"threshold_uncertainty_score":0.9998363,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05069375050295716,"score_gpt":0.3126357782973964,"score_spread":0.2619420277944392,"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."}}