{"id":"W4390597125","doi":"10.5383/juspn.16.02.002","title":"A Filtering Method for Fast Convex Hull Construction","year":2022,"lang":"en","type":"article","venue":"Journal of Ubiquitous Systems and Pervasive Networks","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Convex hull; Polygon (computer graphics); Hull; Vertex (graph theory); Computer science; Computer graphics; Set (abstract data type); Regular polygon; Computational geometry; Speedup; Algorithm; Convex set; Computer graphics (images); Theoretical computer science; Mathematics; Convex optimization; Parallel computing; Geometry; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004533184,0.0001199224,0.0003180203,0.00009875306,0.0001970753,0.00007268049,0.00007975932,0.00006328644,0.00001648388],"category_scores_gemma":[0.00001334875,0.0001144206,0.00009627686,0.0001017664,0.00001599748,0.00007676428,0.00002191004,0.0002326694,1.501667e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008152751,"about_ca_system_score_gemma":0.0000216471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001729247,"about_ca_topic_score_gemma":0.000003600642,"domain_scores_codex":[0.9990346,0.00008654065,0.0004452171,0.00009232965,0.0001617177,0.0001795899],"domain_scores_gemma":[0.9993349,0.0001461875,0.0002051028,0.00007742859,0.0001549478,0.00008138368],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003610986,0.000007311242,0.0001539267,0.0001217746,0.00008407805,0.00001373677,0.0002780856,0.990566,0.001178177,0.0006845281,0.002106838,0.00476943],"study_design_scores_gemma":[0.0005573951,0.0002758841,0.0000398327,0.00007362969,0.00005189357,0.001061651,0.001542526,0.982138,0.0000826832,0.00006610291,0.0139771,0.0001332639],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01856171,0.00276414,0.9749037,0.00004786614,0.003314087,0.0002298259,0.00001627913,0.00002613572,0.0001362367],"genre_scores_gemma":[0.9896879,0.0003404411,0.008403822,0.00006059266,0.001370392,0.00002384482,0.00001065762,0.00004138086,0.00006096342],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9711262,"threshold_uncertainty_score":0.4665937,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01025088592619701,"score_gpt":0.2233157069374328,"score_spread":0.2130648210112358,"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."}}