{"id":"W2981913495","doi":"10.1109/tits.2019.2946259","title":"3D Highway Curve Reconstruction From Mobile Laser Scanning Point Clouds","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Point cloud; Outlier; Computer science; Laser scanning; Advanced driver assistance systems; Artificial intelligence; Computer vision; Estimator; Detector; Point (geometry); Variance (accounting); Lidar; Face (sociological concept); Remote sensing; Laser; Geography; Mathematics; Geometry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004299023,0.001126485,0.000841153,0.004650567,0.0003533298,0.001247152,0.001184013,0.001126671,0.001245272],"category_scores_gemma":[0.001437106,0.0006631647,0.001342526,0.003142118,0.0004670203,0.0009656022,0.001151317,0.001061077,0.002043435],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004510887,"about_ca_system_score_gemma":0.001122658,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009193042,"about_ca_topic_score_gemma":0.009742971,"domain_scores_codex":[0.999139,0.00007119756,0.00003610471,0.0001731828,0.000460967,0.0001196342],"domain_scores_gemma":[0.9989116,0.0001134723,0.0001434449,0.0002675164,0.0005028673,0.00006104627],"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":[0.0002568059,0.0002014496,0.0150488,0.0002949089,0.0002029972,0.0006503038,0.000376019,0.4237782,0.07558543,0.002404749,0.004057111,0.4771432],"study_design_scores_gemma":[0.000009016544,0.00004376557,0.005490949,0.00001486422,0.00001243651,0.0002009689,0.0001313158,0.9751894,0.01594014,0.0009038469,0.00203106,0.00003216109],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2093175,0.0002912171,0.7811304,0.0001144251,0.00004102676,0.0001728332,0.001633293,0.005535109,0.001764207],"genre_scores_gemma":[0.6407388,0.0004401874,0.3515862,0.00003685918,0.0000345761,0.0001299557,0.005343503,0.0004233503,0.001266648],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009193042,"threshold_uncertainty_score":0.01827908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01191133961679817,"score_gpt":0.2267006021673452,"score_spread":0.2147892625505471,"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."}}