{"id":"W4220933764","doi":"10.1155/2022/2266706","title":"Research on the Prediction of the Operational Risk Field of Intelligent Vehicles Based on Dual Multiline LiDAR","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; Guangxi University; Natural Science Foundation of Guangxi Province; National Natural Science Foundation of China","keywords":"Point cloud; Computer science; Field (mathematics); Dual (grammatical number); Artificial intelligence; Computer vision; Cloud computing; Real-time computing; Lidar; Sequence (biology); Feature (linguistics); Simulation; Remote sensing","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.0006272663,0.00005625872,0.0001018913,0.0001191858,0.0001507273,0.000001881526,0.0001489925,0.00004299889,0.00006080118],"category_scores_gemma":[0.00005834021,0.00003694145,0.00008013926,0.000222904,0.00005006572,0.00005441899,0.000003136607,0.0007142276,3.223773e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005435833,"about_ca_system_score_gemma":0.00004702298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004466704,"about_ca_topic_score_gemma":0.00001341293,"domain_scores_codex":[0.9989648,0.00009551722,0.0004021604,0.00005685979,0.0004057814,0.00007483297],"domain_scores_gemma":[0.9991785,0.000380807,0.0001590565,0.0001321032,0.0001362103,0.00001329802],"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":[0.0003738881,0.00008675277,0.0031314,0.00001384248,0.00002409543,0.000001662207,0.0005150902,0.9821887,0.008120773,0.001000685,0.00004821283,0.004494884],"study_design_scores_gemma":[0.001180595,0.002134496,0.4750871,0.0001422579,0.00004890447,0.000003997483,0.002900122,0.0961299,0.4198295,0.001373547,0.001081046,0.00008858107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9953122,0.00005091204,0.003396326,0.0007367419,0.000207488,0.0001458805,0.0000848104,0.00001290148,0.0000527154],"genre_scores_gemma":[0.9994466,0.00006938802,0.000387484,0.00003653639,0.00002753635,0.00001005725,0.000007142297,0.000007746896,0.000007497979],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8860588,"threshold_uncertainty_score":0.3103003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0189325246717383,"score_gpt":0.2787639994913567,"score_spread":0.2598314748196184,"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."}}