{"id":"W4281654129","doi":"10.5194/isprs-archives-xliii-b1-2022-257-2022","title":"AUTOMOTIVE RADAR BASED LEAN DETECTION OF VEHICLES","year":2022,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Artificial intelligence; Radar; Object detection; Automotive industry; Intersection (aeronautics); Computer vision; Minimum bounding box; Pixel; Convolutional neural network; Advanced driver assistance systems; Kernel (algebra); Real-time computing; Pattern recognition (psychology); Engineering; Image (mathematics); Telecommunications; Mathematics","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","sts"],"consensus_categories":[],"category_scores_codex":[0.00143168,0.000370281,0.0004005904,0.0009915573,0.002037117,0.0004349958,0.002817939,0.00006001765,0.000005942453],"category_scores_gemma":[0.0005123616,0.0002555486,0.0003782835,0.001684489,0.002539019,0.0006032709,0.001755578,0.0005278992,0.000001614664],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007292021,"about_ca_system_score_gemma":0.0002839235,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.4910908,"about_ca_topic_score_gemma":0.06559177,"domain_scores_codex":[0.9951153,0.0003787075,0.001413406,0.0004588303,0.00216151,0.0004722788],"domain_scores_gemma":[0.9953786,0.001342968,0.002134097,0.0006589016,0.0003525358,0.000132942],"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":[0.0001029313,0.00002775811,0.00008965957,0.00002383381,0.00005705272,2.349569e-7,0.002149823,0.02005661,0.0062985,0.00003625922,0.00002746755,0.9711299],"study_design_scores_gemma":[0.0006419134,0.0002023484,0.002019423,0.0001220804,0.00002968863,0.0001008809,0.001133943,0.9542944,0.02631292,0.01115459,0.003725944,0.0002618936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00792886,0.00003222472,0.9816852,0.005061487,0.001464258,0.0007016484,0.0001001649,0.0000709519,0.002955222],"genre_scores_gemma":[0.9887887,0.00005497205,0.009699322,0.001285001,0.00008164102,0.000001025649,0.00002457616,0.0000104203,0.0000543847],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9808598,"threshold_uncertainty_score":0.9999897,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01605219941642698,"score_gpt":0.2492237841207584,"score_spread":0.2331715847043314,"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."}}