{"id":"W1486636865","doi":"10.1007/3-540-45749-6_28","title":"Geometric Algorithms for Density-Based Data Clustering","year":2002,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Approximation algorithm; Cluster analysis; Algorithm; Computer science; Quadratic equation; Function (biology); 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","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.001497284,0.0006077329,0.0005921655,0.002052295,0.0003634933,0.001407957,0.0112693,0.0002464237,0.0000235145],"category_scores_gemma":[0.0001646346,0.0005858039,0.0001280414,0.001424205,0.0003751276,0.001619575,0.006237873,0.0005458797,0.00006667342],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002138634,"about_ca_system_score_gemma":0.0002209526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000174041,"about_ca_topic_score_gemma":0.00003715589,"domain_scores_codex":[0.994705,0.00002156534,0.0005628167,0.002652851,0.001149115,0.0009086296],"domain_scores_gemma":[0.9943749,0.0006389309,0.0003189678,0.004255349,0.0002202903,0.0001915402],"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":[0.000002621039,0.00003213919,0.000009412006,0.00007510651,0.00001470043,0.00007003385,0.00003623556,0.0112158,0.000004877734,0.001860191,0.0003927071,0.9862862],"study_design_scores_gemma":[0.0004357317,0.0001323775,0.00002341181,0.0001758354,0.0000172576,0.00001649632,3.917477e-8,0.9679729,0.000156131,0.01225798,0.01811391,0.0006979054],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000001372165,0.0002987408,0.9940196,0.0008171462,0.002663065,0.00073114,0.00008099743,0.0002460939,0.001141815],"genre_scores_gemma":[0.001534123,0.00004665878,0.9947217,0.001799059,0.0007860182,0.00001399913,0.0001559296,0.00004802242,0.0008945505],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9855883,"threshold_uncertainty_score":0.9996594,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06445117085016047,"score_gpt":0.2762087730906237,"score_spread":0.2117576022404632,"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."}}