{"id":"W3195513965","doi":"10.3390/su13169259","title":"Safety Assessment of Urban Intersection Sight Distance Using Mobile LiDAR Data","year":2021,"lang":"en","type":"article","venue":"Sustainability","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Intersection (aeronautics); Visibility; Lidar; Computer science; Point cloud; Transport engineering; Point (geometry); Remote sensing; Computer vision; Geography; Engineering; Mathematics; Meteorology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004236247,0.00008770583,0.0001367015,0.00001244696,0.0001459687,0.00002010573,0.000224089,0.00004312127,0.0003139101],"category_scores_gemma":[0.0001666943,0.00008659482,0.00004277314,0.0003471168,0.000232953,0.0001803447,0.0004362067,0.0001167546,0.000003843962],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001339743,"about_ca_system_score_gemma":0.0001869386,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007161318,"about_ca_topic_score_gemma":0.0002037079,"domain_scores_codex":[0.9987801,0.0001312897,0.000253591,0.0004554999,0.000205482,0.0001740317],"domain_scores_gemma":[0.9984641,0.00005767253,0.000090282,0.001239937,0.00009178612,0.00005621697],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006930794,0.001430666,0.8663198,0.0003254307,0.00005918523,0.0000286961,0.002971791,0.01865703,0.03177081,0.002322966,0.001369256,0.07467506],"study_design_scores_gemma":[0.0004534004,0.00009222331,0.7214223,0.00003227625,0.00005898777,0.00002073275,0.006657778,0.09580392,0.007833195,0.005945637,0.1612481,0.000431457],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9030426,0.00003090483,0.09073365,0.0002223671,0.00008510175,0.0003130379,0.00003779931,0.00003094405,0.00550362],"genre_scores_gemma":[0.9942082,0.000003552072,0.005306575,0.00001571804,0.00002449693,0.00000233741,0.00005727096,0.00000767848,0.0003741766],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1598788,"threshold_uncertainty_score":0.3531234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01413721457315482,"score_gpt":0.3075892700505057,"score_spread":0.2934520554773509,"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."}}