{"id":"W2899724367","doi":"10.1007/978-3-030-03801-4_43","title":"Scale-Aware RPN for Vehicle Detection","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Bootstrapping (finance); Disjoint sets; Scale (ratio); Artificial intelligence; Data mining; Feature (linguistics); Machine learning","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":[0.0004765652,0.001113506,0.000897498,0.0005369272,0.0003069961,0.0005887212,0.001572958,0.001013118,0.004057185],"category_scores_gemma":[0.001108716,0.0004398438,0.0006942125,0.0007681305,0.0002311159,0.001088227,0.0008953746,0.001066229,0.002825544],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000376539,"about_ca_system_score_gemma":0.0004282384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003735529,"about_ca_topic_score_gemma":0.00542231,"domain_scores_codex":[0.9996988,0.00003263348,0.00001369861,0.0001199944,0.00008602322,0.00004894645],"domain_scores_gemma":[0.9997287,0.00006779685,0.00001725133,0.00006576688,0.0001065214,0.00001379994],"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":[0.0001852002,0.0001218508,0.0008215166,0.0001423021,0.00008597912,0.0001015451,0.00003122477,0.08598737,0.02609164,0.00317964,0.01410412,0.8691477],"study_design_scores_gemma":[0.000006235924,0.00003549778,0.000807069,0.0000141056,0.00002835639,0.00009622234,0.00001277165,0.9831811,0.009040616,0.003717155,0.003048558,0.00001237187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01585069,0.001597691,0.9712799,0.0001907513,0.00054481,0.00005990441,0.0005285399,0.005088669,0.004858995],"genre_scores_gemma":[0.4626992,0.00143862,0.5162628,0.0005019907,0.000463953,0.0001506937,0.001969075,0.0005703605,0.01594329],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004057185,"threshold_uncertainty_score":0.01357269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01870270601034568,"score_gpt":0.2626382747578919,"score_spread":0.2439355687475462,"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."}}