{"id":"W2776336566","doi":"10.15353/vsnl.v3i1.181","title":"Efficient Deep Network Architecture for Vision-Based Vehicle Detection Keyvan Kasiri,","year":2017,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Ontario Centres of Excellence","keywords":"Deep learning; Artificial intelligence; Computer science; Software deployment; Artificial neural network; Architecture; Object detection; Deep neural networks; Process (computing); Network architecture; Machine learning; Pattern recognition (psychology); Computer security","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004817114,0.0001224928,0.0002256198,0.0001238122,0.0005747374,0.0001994956,0.000162424,0.00006584756,0.000001714841],"category_scores_gemma":[0.00004971217,0.0001014815,0.00009056729,0.00004411576,0.00009213006,0.00008633586,0.00002048527,0.000214852,0.000001652037],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004961353,"about_ca_system_score_gemma":0.00003521575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002735663,"about_ca_topic_score_gemma":0.000001576413,"domain_scores_codex":[0.999144,0.00003080317,0.0003876713,0.00009356314,0.0001795883,0.0001643854],"domain_scores_gemma":[0.9991366,0.0002056249,0.0002867772,0.0001293019,0.0001705274,0.0000711611],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004550725,0.00001559976,0.0005424539,0.00004952129,0.00002339685,0.000004272489,0.0000465628,0.9421707,0.000429159,0.0002012355,0.0002259494,0.05624567],"study_design_scores_gemma":[0.0008543732,0.0001018837,0.01820102,0.0001655707,0.00001672697,0.00009903181,0.00003479121,0.9765328,0.00009673748,0.001497626,0.002292573,0.0001068218],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2433831,0.0008472194,0.7541676,0.0005372667,0.0007972574,0.0001337973,0.000002911026,0.00006280557,0.00006802715],"genre_scores_gemma":[0.9945415,0.00000349851,0.005119661,0.00004745697,0.00025857,0.000003429446,0.000001783469,0.00001829546,0.000005843988],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7511583,"threshold_uncertainty_score":0.4420474,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004742313726864791,"score_gpt":0.2403935391249207,"score_spread":0.2356512253980559,"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."}}