{"id":"W3026393200","doi":"10.1016/j.autcon.2020.103250","title":"LiDAR-equipped UAV path planning considering potential locations of defects for bridge inspection","year":2020,"lang":"en","type":"article","venue":"Automation in Construction","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":207,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Concordia University","keywords":"Visibility; Lidar; Bridge (graph theory); Computer science; Path (computing); Motion planning; Viewpoints; Ranging; Sampling (signal processing); Real-time computing; Genetic algorithm; Computer vision; Artificial intelligence; Simulation; Remote sensing; Robot; Machine learning","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":[0.00007294827,0.0001101254,0.0001543079,0.0001409606,0.00007363963,0.00002543361,0.0000471351,0.00008323632,0.000005448969],"category_scores_gemma":[0.00008247476,0.0001317385,0.00004737562,0.0002273111,0.00004007679,0.0002722024,0.00001106195,0.0001123035,0.000002589337],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008384553,"about_ca_system_score_gemma":0.00003059111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001169563,"about_ca_topic_score_gemma":0.000002398819,"domain_scores_codex":[0.9992443,0.00001475971,0.0003431952,0.0001412811,0.00009913124,0.0001573067],"domain_scores_gemma":[0.9996722,0.0000373662,0.00009493019,0.00008069074,0.00007766068,0.00003712062],"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":[0.00006155437,0.00001413027,0.02863069,0.000740211,0.0000727937,0.000006987927,0.003792727,0.7417206,0.1876156,0.005629948,0.0006539364,0.03106077],"study_design_scores_gemma":[0.00156421,0.00009990131,0.09111233,0.0003291841,0.00004724599,0.00007760711,0.001476801,0.7825563,0.1199895,0.001978462,0.0003976857,0.0003708073],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6787917,0.00003826215,0.3193669,0.00004343719,0.0009151809,0.0002213916,0.000008100867,0.0003633098,0.0002516953],"genre_scores_gemma":[0.9882074,0.000008390102,0.01142048,0.0000191109,0.0002608311,0.00003909161,0.00002392496,0.00001999989,7.623255e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3094157,"threshold_uncertainty_score":0.5372139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01447947265697696,"score_gpt":0.2364202046838427,"score_spread":0.2219407320268657,"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."}}