{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008142937,0.0005345102,0.0003920453,0.002968031,0.0004048265,0.0008241609,0.0006998185,0.0004739302,0.0005145379],"category_scores_gemma":[0.002432804,0.0002270901,0.0004308845,0.001161705,0.0002218999,0.0009574454,0.001079121,0.0002757777,0.0002260476],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004864023,"about_ca_system_score_gemma":0.000740459,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005174379,"about_ca_topic_score_gemma":0.005976239,"domain_scores_codex":[0.9989358,0.0001883641,0.00006185107,0.0001555766,0.0005414779,0.0001170422],"domain_scores_gemma":[0.9985216,0.0003096122,0.000373186,0.0001336617,0.0005962526,0.00006567689],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0004946467,0.0005163698,0.3231692,0.0003387876,0.0002212315,0.0005717734,0.0006372792,0.2868255,0.03664395,0.002862811,0.001280537,0.3464378],"study_design_scores_gemma":[0.0000180117,0.0004310786,0.09129984,0.0000428834,0.00006739047,0.0002397005,0.0007101471,0.8861624,0.01762749,0.001503735,0.001835774,0.00006155771],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8385063,0.0002119216,0.157014,0.00007162759,0.00002178096,0.0001147576,0.000728477,0.0006433107,0.002687672],"genre_scores_gemma":[0.9752312,0.0000560658,0.0239847,0.000007800036,0.000005716432,0.00002925896,0.000485189,0.000009642713,0.0001903867],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005174379,"threshold_uncertainty_score":0.01028848,"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."}}