{"id":"W2803022227","doi":"10.1177/0361198118758685","title":"Automated Extraction of Horizontal Curve Attributes using LiDAR Data","year":2018,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Alberta Innovates","keywords":"Lidar; Computer science; Ranging; Code (set theory); Data mining; Remote sensing; Geology; Set (abstract data type); Telecommunications","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":[0.004720392,0.0001882304,0.0003410787,0.000422383,0.0007311827,0.00007939473,0.001493781,0.0001658801,0.0006515004],"category_scores_gemma":[0.0002226341,0.0001465528,0.0001888116,0.002428646,0.001562028,0.0009085051,0.00002925864,0.001201548,0.0000713811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002799568,"about_ca_system_score_gemma":0.0003123179,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02769748,"about_ca_topic_score_gemma":0.03603507,"domain_scores_codex":[0.9936667,0.0008958167,0.001212958,0.0004590259,0.003127592,0.0006379188],"domain_scores_gemma":[0.9962292,0.000592765,0.0005935472,0.0009408037,0.001371428,0.0002723081],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001584166,0.0008123203,0.5403069,0.0001570363,0.0002504655,0.00006750728,0.003853881,0.002150984,0.4060559,0.0004822212,0.02574616,0.01853249],"study_design_scores_gemma":[0.0007913435,0.0005938396,0.949221,0.0002087082,0.0000698785,0.000003063773,0.001098271,0.009994046,0.02070281,0.0008530121,0.01627817,0.0001859171],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9916764,0.00004601708,0.005784896,0.001003656,0.0003470568,0.0005947074,0.0001411242,0.00004441389,0.0003617246],"genre_scores_gemma":[0.9873841,0.0001481164,0.01199689,0.00001323329,0.0002177135,0.000003868872,0.0000476921,0.00003783352,0.0001505791],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.408914,"threshold_uncertainty_score":0.9815547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1552649451418209,"score_gpt":0.4238292489376109,"score_spread":0.2685643037957899,"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."}}