{"id":"W7018028875","doi":"","title":"CHAN's PLANAR CONVEX HULL ALGORITHM: A Brief Survey and Sequential Experimental Comparison","year":2006,"lang":"en","type":"article","venue":"NPARC","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Convex hull; Planar; Hull; Convex set; Correctness; Set (abstract data type); Regular polygon","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004142566,0.001557326,0.001478398,0.004517735,0.001344332,0.002622542,0.002962516,0.001467386,0.01120585],"category_scores_gemma":[0.02004777,0.0008769906,0.0008094469,0.01145202,0.001289764,0.007047156,0.002233381,0.00179697,0.003679976],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001793722,"about_ca_system_score_gemma":0.00243204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005734807,"about_ca_topic_score_gemma":0.004751852,"domain_scores_codex":[0.9943539,0.001181884,0.0005156797,0.0007297155,0.002973262,0.0002456891],"domain_scores_gemma":[0.9918342,0.003656937,0.0003187762,0.001568884,0.002448186,0.0001730735],"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":[0.0006157605,0.0002565022,0.001524471,0.0006884622,0.00009406322,0.00005904418,0.0001662752,0.01710319,0.004720151,0.01841494,0.01279928,0.9435578],"study_design_scores_gemma":[0.0004254518,0.002323048,0.01061442,0.0004853186,0.0003064581,0.002413592,0.0009992318,0.6232818,0.07420408,0.07650688,0.2080208,0.0004189257],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02278096,0.01362001,0.9337741,0.0006258023,0.0003606911,0.000825379,0.0008730261,0.004810825,0.02232919],"genre_scores_gemma":[0.1135236,0.0138182,0.8603353,0.0003201869,0.0002227047,0.000979422,0.003249329,0.00121381,0.006337476],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01120585,"threshold_uncertainty_score":0.03748733,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02496564069630522,"score_gpt":0.2719063722001711,"score_spread":0.2469407315038658,"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."}}