{"id":"W4382203389","doi":"10.1109/tits.2023.3286384","title":"A Virtual Method for Optimizing Deployment of Roadside Monitoring Lidars at As-Built Intersections","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Transportation Safety and Impact Analysis","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Fundamental Research Funds for the Central Universities; Natural Sciences and Engineering Research Council of Canada","keywords":"Software deployment; Computer science; Lidar; Transport engineering; Engineering; Remote sensing; Geology; Operating system","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003407848,0.000326644,0.0004790002,0.0008049615,0.0002267576,0.0000351635,0.0001708182,0.0001721383,0.00009623581],"category_scores_gemma":[0.000004114262,0.0003581723,0.0005277556,0.001055487,0.00003371918,0.00026219,2.473899e-7,0.0002051529,0.00009936869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002262304,"about_ca_system_score_gemma":0.00003512104,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000360175,"about_ca_topic_score_gemma":0.0004790029,"domain_scores_codex":[0.9976708,0.00005070788,0.001123338,0.0003429424,0.0004359775,0.0003762823],"domain_scores_gemma":[0.9988714,0.0003279051,0.0001443984,0.0002946934,0.0001854675,0.0001761021],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001428724,0.00005743312,0.000159074,0.0001618148,0.000552813,0.00000359126,0.004888349,0.9696425,0.01978493,0.0001064757,0.00007270328,0.004427446],"study_design_scores_gemma":[0.001048442,0.0004363175,0.001305121,0.000502572,0.0006972872,0.000009411689,0.0136316,0.256974,0.7224851,0.00003407963,0.002189817,0.0006862693],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1796993,0.00007111461,0.8164555,0.0000287508,0.002131749,0.0005955806,0.0002890893,0.0006575072,0.00007143356],"genre_scores_gemma":[0.9958863,0.0004737054,0.002001064,0.000009066775,0.00006753229,0.0005607829,0.000110284,0.00009856877,0.0007926827],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.816187,"threshold_uncertainty_score":0.999887,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03603429093509477,"score_gpt":0.3056624985471887,"score_spread":0.269628207612094,"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."}}