{"id":"W4411291366","doi":"10.1061/jpeodx.pveng-1573","title":"Tile-Based Pavement Rutting and Roughness Evaluation Using Mobile LiDAR Data","year":2025,"lang":"en","type":"article","venue":"Journal of Transportation Engineering Part B Pavements","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Tile; Lidar; Rut; Geotechnical engineering; Geology; Remote sensing; Surface finish; Environmental science; Engineering; Cartography; Geography; Archaeology; Mechanical engineering","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001031033,0.000103006,0.0001489196,0.00009460667,0.00008049535,0.00004817057,0.0001442372,0.00003564971,0.000133557],"category_scores_gemma":[0.00002078369,0.00008870077,0.00003245407,0.0001676968,0.00001038827,0.0003458683,0.000002043005,0.00009859283,0.000001172045],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001243373,"about_ca_system_score_gemma":0.00006985003,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007542105,"about_ca_topic_score_gemma":0.00009220023,"domain_scores_codex":[0.9989077,0.00004075581,0.0004236165,0.0001358901,0.0003559787,0.0001360125],"domain_scores_gemma":[0.9994845,0.0000536763,0.0001637829,0.000123749,0.0001158801,0.00005839036],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00002613381,0.00003010955,0.3438159,0.0001375439,0.00008554393,0.00000929665,0.000247461,0.6409976,0.0007985628,0.000003486608,0.00005755232,0.01379073],"study_design_scores_gemma":[0.001132191,0.00009697565,0.5277109,0.0004784166,0.0001952355,0.000002344689,0.0002495335,0.4644477,0.001197472,0.00001470812,0.004301672,0.000172878],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9878727,0.0007440756,0.01044321,0.00002338907,0.0006367267,0.0001540171,0.00009369649,0.00001275045,0.00001944876],"genre_scores_gemma":[0.9961192,0.0000263648,0.003381707,0.00004261385,0.0000594755,0.000001095119,0.0003473689,0.000003127979,0.00001907077],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1838949,"threshold_uncertainty_score":0.3617112,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04937910186839798,"score_gpt":0.2850505019739415,"score_spread":0.2356714001055435,"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."}}