{"id":"W4410067472","doi":"10.1016/j.trgeo.2025.101574","title":"Scanners, satellites, smart compactors, and drones: Emerging technologies for assessing compacted soil lift thickness","year":2025,"lang":"en","type":"article","venue":"Transportation Geotechnics","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Research Council Canada; Federal Highway Administration; Mid-Atlantic Transportation Sustainability University Transportation Center; Delaware Department of Transportation; National Research Council","keywords":"Lift (data mining); Drone; Environmental science; Remote sensing; Engineering; Computer science; Geology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000316653,0.0001770224,0.000239715,0.000127368,0.0003676152,0.0001226459,0.0001577985,0.0001800172,0.00003610264],"category_scores_gemma":[0.00003976465,0.0001547052,0.000058311,0.0004472898,0.0001226267,0.0003163659,0.000002029891,0.0002035407,0.000002994772],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006856133,"about_ca_system_score_gemma":0.00005399787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008496672,"about_ca_topic_score_gemma":0.004236325,"domain_scores_codex":[0.9989567,0.00003351996,0.0002984727,0.0002796072,0.000146861,0.0002848857],"domain_scores_gemma":[0.9994401,0.0001815551,0.00009289228,0.0001521365,0.00008681721,0.00004648539],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000927006,0.00003850303,0.7741401,0.0004810493,0.00007073357,0.000007099142,0.0009747571,0.008988635,0.001146576,0.001021713,0.0005563311,0.2124818],"study_design_scores_gemma":[0.0005539174,0.00005546632,0.960184,0.0002595389,0.00007408261,0.00000277604,0.004520124,0.0197965,0.0008670271,0.002817947,0.01047957,0.0003890286],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9779704,0.00258061,0.01653818,0.0008081817,0.0002545702,0.0003639425,0.000237919,0.0007842904,0.0004619152],"genre_scores_gemma":[0.9956515,0.0002946951,0.00275387,0.00008657149,0.00001015643,0.000004658147,0.001046421,0.000005339779,0.0001467548],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2120928,"threshold_uncertainty_score":0.6308694,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01620321883426419,"score_gpt":0.2531519437850864,"score_spread":0.2369487249508222,"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."}}