{"id":"W2066449891","doi":"10.1007/s00453-008-9208-9","title":"An Improved Algorithm for Online Unit Clustering","year":2008,"lang":"en","type":"article","venue":"Algorithmica","topic":"Optimization and Search Problems","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Competitive analysis; Cluster analysis; Theory of computation; Online algorithm; Partition (number theory); Mathematics; Combinatorics; Dimension (graph theory); Upper and lower bounds; Randomized algorithm; Algorithm; Sequence (biology); Unit (ring theory); Approximation algorithm; Computer science; Discrete mathematics; Statistics","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":[0.0001872717,0.000139995,0.0001552485,0.0001138426,0.0002785972,0.00009098003,0.000810815,0.00006980863,0.00001809455],"category_scores_gemma":[0.00002124117,0.0001356948,0.00006199592,0.0003132056,0.00005347885,0.0006041958,0.0001474911,0.0001237955,0.00001703211],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002428577,"about_ca_system_score_gemma":0.0001046702,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003597745,"about_ca_topic_score_gemma":0.00001049771,"domain_scores_codex":[0.9987698,0.00004937756,0.0002265004,0.0003954861,0.0001792003,0.0003796574],"domain_scores_gemma":[0.9989448,0.00006567279,0.00005897268,0.000523699,0.000205865,0.0002010565],"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":[0.000003580853,0.0002220645,0.0000096282,0.000007730828,0.00001761701,0.00001189001,0.000401477,0.0009615886,0.0006432749,0.0005582544,0.0004003099,0.9967626],"study_design_scores_gemma":[0.0006704311,0.0002625377,0.0001128246,0.000004792586,0.000002270713,0.00004418909,0.00001606792,0.9874464,0.00059911,0.0003858162,0.01026955,0.0001860458],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004153193,0.00002792571,0.9978885,0.0005391608,0.0002569464,0.0003569836,0.0000313199,0.0003136054,0.0001702054],"genre_scores_gemma":[0.003910469,0.00003200044,0.994266,0.000655176,0.000210663,0.00004315049,0.00007011587,0.00002075974,0.0007916876],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9965765,"threshold_uncertainty_score":0.5533475,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04195344232629394,"score_gpt":0.2997598735524124,"score_spread":0.2578064312261185,"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."}}