{"id":"W4281696822","doi":"10.1016/j.jag.2022.102836","title":"SD-GCN: Saliency-based dilated graph convolution network for pavement crack extraction from 3D point clouds","year":2022,"lang":"en","type":"article","venue":"International Journal of Applied Earth Observation and Geoinformation","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Xiamen University; National Natural Science Foundation of China","keywords":"Point cloud; Leverage (statistics); Dilation (metric space); Convolution (computer science); Feature extraction; Computer science; Graph; Artificial intelligence; Pattern recognition (psychology); Computer vision; Mathematics; Artificial neural network; Geometry; Theoretical computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003815446,0.0001248657,0.0001394663,0.0001562127,0.0001679078,0.00007989917,0.0001276264,0.0000513798,0.00008880832],"category_scores_gemma":[0.00001439674,0.000128094,0.00006973714,0.0001275112,0.00001454567,0.0005453435,0.00002164878,0.0002098248,0.000002412279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001416425,"about_ca_system_score_gemma":0.00004394287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000140091,"about_ca_topic_score_gemma":0.000007721807,"domain_scores_codex":[0.9987128,0.0000116994,0.0006138679,0.0000793299,0.0004275675,0.0001547765],"domain_scores_gemma":[0.9991338,0.00006238871,0.0003932956,0.00006932073,0.0002936977,0.00004754438],"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":[0.0003143966,0.00001798002,0.0007479049,0.00001934547,0.0001015238,0.000001335973,0.0005751338,0.9610573,0.004593309,0.002338285,0.001640749,0.02859274],"study_design_scores_gemma":[0.003940386,0.0002375232,0.07470459,0.00008588861,0.00007171992,0.00002479519,0.0009781468,0.8032067,0.008668835,0.01014968,0.09753539,0.0003963535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6508383,0.00007457853,0.3438595,0.00033569,0.003968473,0.0003489622,0.00007319119,0.00007739596,0.0004240086],"genre_scores_gemma":[0.9829879,0.00004750606,0.01531867,0.0004971242,0.0006076975,0.00003631745,0.0004796983,0.00001366979,0.00001136716],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3321497,"threshold_uncertainty_score":0.5223521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008712121307573921,"score_gpt":0.2126980240529828,"score_spread":0.2039859027454089,"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."}}