{"id":"W2295016007","doi":"","title":"Bichromatic Line Segment Intersection Counting in O(n sqrt(log n)) Time.","year":2011,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Intersection (aeronautics); Combinatorics; Line (geometry); Binary logarithm; Point (geometry); Line segment; Reduction (mathematics); Time complexity; Mathematics; Algorithm; Computer science; Discrete mathematics; Geometry","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":[0.0003324076,0.00007386076,0.00008139698,0.0001434621,0.00002874752,0.00007643682,0.0004481261,0.00001829776,0.000311114],"category_scores_gemma":[0.00001295576,0.00006263574,0.00001935716,0.0002849427,0.00001148408,0.0007120867,0.0003126813,0.00005194599,0.0005879944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003566767,"about_ca_system_score_gemma":0.000007658179,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001771411,"about_ca_topic_score_gemma":0.00004654593,"domain_scores_codex":[0.9993134,0.00001952164,0.0001714669,0.0002093795,0.0001257035,0.000160563],"domain_scores_gemma":[0.9996195,0.00001279921,0.00004395443,0.0002781051,0.00001975879,0.00002583507],"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":[0.00002207741,0.001452684,0.006584087,0.0002111741,0.00014835,0.0002381466,0.0110914,0.0001162138,0.004536904,0.1083504,0.05139294,0.8158556],"study_design_scores_gemma":[0.0006088933,0.0001867164,0.006512693,0.00008082869,0.000008223871,0.000008322677,0.0002389504,0.9823846,0.003683629,0.003618196,0.00234633,0.0003226677],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02602522,0.00001303005,0.9038465,0.000258736,0.0004779793,0.0001770242,5.385128e-7,0.0002466983,0.06895428],"genre_scores_gemma":[0.7481303,0.000009107834,0.2422015,0.0008977048,0.0001066836,0.0000224997,0.00001221918,0.00001163642,0.008608423],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9822683,"threshold_uncertainty_score":0.7557675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0261794865334309,"score_gpt":0.221253031092648,"score_spread":0.1950735445592171,"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."}}