{"id":"W2950401571","doi":"10.3390/rs11121453","title":"Automated Visual Recognizability Evaluation of Traffic Sign Based on 3D LiDAR Point Clouds","year":2019,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Computer science; Visibility; Traffic sign; Point cloud; Computer vision; Sign (mathematics); Point (geometry); Artificial intelligence; Scale (ratio); Geography; Optics; Mathematics; Cartography; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007758726,0.000718788,0.0007003156,0.002818033,0.0002785149,0.001294401,0.0006355715,0.0004818164,0.0007836057],"category_scores_gemma":[0.002897398,0.0002666227,0.0007001911,0.001007455,0.0004922813,0.001186195,0.001050018,0.0004914386,0.0003816438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004776592,"about_ca_system_score_gemma":0.0005742285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003420769,"about_ca_topic_score_gemma":0.002929072,"domain_scores_codex":[0.9987356,0.0002318834,0.00006625596,0.0001747991,0.00066669,0.0001247421],"domain_scores_gemma":[0.998099,0.0003539156,0.0003414572,0.0002659945,0.0008285273,0.0001110836],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005604567,0.000185752,0.02332639,0.0003177446,0.000100315,0.00033483,0.0003902221,0.1169005,0.2720075,0.0027972,0.001794772,0.5812844],"study_design_scores_gemma":[0.00002242628,0.0001367219,0.02560432,0.00002523797,0.00003691873,0.0001952274,0.0002161195,0.9103621,0.0612679,0.001188857,0.0008852571,0.0000588457],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3119308,0.0001862251,0.6830747,0.00007115462,0.00004056194,0.0001729454,0.0002796806,0.002511276,0.001732613],"genre_scores_gemma":[0.9038414,0.0001251088,0.09493296,0.0000219695,0.00001692532,0.00007170464,0.000492035,0.00009204521,0.0004059007],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003420769,"threshold_uncertainty_score":0.006801724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01771850614579003,"score_gpt":0.2812161044803914,"score_spread":0.2634975983346013,"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."}}