{"id":"W4407242508","doi":"10.1016/j.autcon.2025.106006","title":"Cost-effective LiDAR for pothole detection and quantification using a low-point-density approach","year":2025,"lang":"en","type":"article","venue":"Automation in Construction","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pothole (geology); Lidar; Point (geometry); Computer science; Remote sensing; Environmental science; Geography; Mathematics; Geology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001843773,0.0001053937,0.0001246354,0.0002616772,0.0001167885,0.00005053535,0.00002825473,0.0001101697,8.222448e-7],"category_scores_gemma":[0.0000588948,0.0001193388,0.00002612486,0.0002630937,0.00004385573,0.0002968991,0.000009382838,0.0001041406,6.393512e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002362795,"about_ca_system_score_gemma":0.00001435272,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002355788,"about_ca_topic_score_gemma":0.00001316373,"domain_scores_codex":[0.9994032,0.00002716484,0.0002099587,0.0001719432,0.00005389198,0.0001338446],"domain_scores_gemma":[0.9997022,0.00005104978,0.00005138433,0.0001005611,0.00007849637,0.00001628106],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006244765,0.0000166974,0.006763273,0.0006633776,0.00004914088,4.029841e-7,0.0007858528,0.05758281,0.2221115,0.005007832,0.00002087663,0.7069358],"study_design_scores_gemma":[0.0005917051,0.00001020746,0.04122442,0.0001126311,0.0000249768,0.00002673446,0.0006319975,0.8295128,0.124976,0.002605299,0.0001392435,0.0001440075],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4689348,0.0000228697,0.5293102,0.000006975594,0.0007086159,0.0007216671,0.000002415903,0.0001375082,0.0001549094],"genre_scores_gemma":[0.9808116,0.000008781126,0.01881484,0.000007788492,0.00006972653,0.000261441,0.0000103461,0.00001084834,0.000004653045],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.77193,"threshold_uncertainty_score":0.4866494,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01167603968318267,"score_gpt":0.2562175853095121,"score_spread":0.2445415456263294,"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."}}