{"id":"W2027251975","doi":"10.1109/tits.2014.2328589","title":"Automated Road Information Extraction From Mobile Laser Scanning Data","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":163,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Road surface; Point cloud; Feature extraction; Laser scanning; Computer vision; Artificial intelligence; Computer science; Feature (linguistics); Engineering; Laser","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003346287,0.0002184308,0.0002006955,0.0001177872,0.0003132373,0.0001293312,0.0003128751,0.0001455649,0.0004840934],"category_scores_gemma":[0.000004531077,0.0002192341,0.00007519139,0.0003812888,0.00006997459,0.001082396,8.893512e-7,0.0002249372,0.00235351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001430452,"about_ca_system_score_gemma":0.00001572101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006591316,"about_ca_topic_score_gemma":0.0005829165,"domain_scores_codex":[0.9980605,0.0001016847,0.0006832094,0.0004263096,0.0004992473,0.0002290059],"domain_scores_gemma":[0.9987055,0.000104802,0.0002220674,0.0007905493,0.0000404548,0.0001366019],"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":[0.00004039508,0.0001293225,0.0001614522,0.00001617678,0.00004157051,7.271265e-7,0.001367115,0.8698823,0.002753427,0.00001289737,0.001611568,0.1239831],"study_design_scores_gemma":[0.000374985,0.00009149346,0.006015303,0.00008892259,0.000109034,0.000005854627,0.001113406,0.8622518,0.02073625,0.0000197299,0.1087824,0.0004109003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1915076,0.000008485565,0.8046993,0.00004088335,0.0009077402,0.0005174949,0.0003029638,0.0005955491,0.001419962],"genre_scores_gemma":[0.9973305,0.00002788927,0.001223386,0.00008627254,0.00005911662,0.00006673298,0.000875026,0.00002473587,0.0003063679],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8058229,"threshold_uncertainty_score":0.9984233,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02065260095448023,"score_gpt":0.2676956295889906,"score_spread":0.2470430286345103,"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."}}