{"id":"W4381192944","doi":"10.32920/23542035","title":"Potential Use of LiDAR Data for Crack Detection: A Case Study on Pavement Cracks","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; 3v Geomatics (Canada)","funders":"King Abdulaziz University; Saudi Arabian Cultural Bureau","keywords":"Lidar; Ranging; Point cloud; Remote sensing; Support vector machine; Artificial intelligence; Computer science; Pixel; Classifier (UML); Pattern recognition (psychology); Object detection; Computer vision; Geology","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":[0.001015077,0.0003293593,0.0003729453,0.001017765,0.0006280952,0.0006527829,0.0006522969,0.001585369,0.0007652587],"category_scores_gemma":[0.002554025,0.0002320965,0.0004263513,0.0008524688,0.0005275958,0.0008803331,0.000472042,0.0005630777,0.0001722557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004630136,"about_ca_system_score_gemma":0.0002798426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004194523,"about_ca_topic_score_gemma":0.009894424,"domain_scores_codex":[0.9992071,0.0002498285,0.00004632178,0.0001230267,0.0003141124,0.00005960378],"domain_scores_gemma":[0.9959962,0.002660824,0.0002108857,0.0002836647,0.0007402288,0.0001082525],"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":[0.001360169,0.002360919,0.1974926,0.001352296,0.0002348244,0.05572971,0.005740117,0.1188528,0.1612142,0.005094402,0.006134192,0.4444338],"study_design_scores_gemma":[0.0001285102,0.001611693,0.1181813,0.0001697669,0.0002177885,0.01289124,0.006494982,0.702657,0.1429292,0.002478158,0.01205959,0.0001807925],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9733821,0.0003994494,0.0226158,0.0005382592,0.00001965729,0.0001179953,0.0002328697,0.0001759816,0.002517911],"genre_scores_gemma":[0.9788234,0.0001509055,0.02002526,0.00004061389,0.00000954492,0.00001939942,0.00009434812,0.00001247243,0.00082397],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004194523,"threshold_uncertainty_score":0.00834024,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08800881426888042,"score_gpt":0.3109029363030759,"score_spread":0.2228941220341955,"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."}}