{"id":"W2294551309","doi":"10.1109/tim.2016.2514780","title":"Extending the Detection Range of Vision-Based Vehicular Instrumentation","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Track (disk drive); Range (aeronautics); Pedestrian detection; Tracking (education); Lens (geology); Pedestrian; Real-time computing; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00102451,0.0001276879,0.0001197273,0.0001658876,0.0002650823,0.00005920592,0.0001593234,0.00004340505,0.00001652732],"category_scores_gemma":[0.00001269956,0.00007955306,0.00007579325,0.0002679078,0.00006846389,0.0003935807,0.00000163307,0.00007494733,0.000006545465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001290072,"about_ca_system_score_gemma":0.00004801583,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003452583,"about_ca_topic_score_gemma":0.00006841057,"domain_scores_codex":[0.9984446,0.0002424928,0.0002969658,0.0002622535,0.0006057768,0.0001478759],"domain_scores_gemma":[0.9992688,0.0001177327,0.000145052,0.0002735301,0.0001412082,0.00005373636],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004236669,0.00007096698,0.0003133603,0.00001551093,0.0000284845,3.984181e-7,0.0001731176,0.0003928055,0.08945101,0.0002178888,0.00000280949,0.9092913],"study_design_scores_gemma":[0.002981585,0.0004934707,0.03444138,0.0002095707,0.00004150883,0.00000852869,0.0001178352,0.008221511,0.952225,0.0006038444,0.0004279421,0.0002277902],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09721728,0.0000342266,0.9007106,0.001022241,0.0006354646,0.0002609483,0.000003582517,0.00005979667,0.00005582021],"genre_scores_gemma":[0.9946049,0.00008100252,0.005012449,0.0002032958,0.00001454069,0.00006536048,2.546153e-7,0.000007815288,0.00001041072],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9090635,"threshold_uncertainty_score":0.324408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03882222739430412,"score_gpt":0.2865221671387475,"score_spread":0.2476999397444433,"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."}}