{"id":"W2126069365","doi":"10.1002/atr.1327","title":"Computer vision approach for the classification of bike type (motorized versus non‐motorized) during busy traffic in the city of Shanghai","year":2015,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Intersection (aeronautics); Robustness (evolution); Transport engineering; Computer science; Binary classification; Artificial intelligence; Data collection; Machine learning; Engineering; Simulation; Support vector machine; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0005019658,0.0005837899,0.0004548025,0.00386056,0.000334764,0.0007846977,0.0004437422,0.0004162635,0.0006087413],"category_scores_gemma":[0.0007219621,0.0001732928,0.0004580015,0.001137168,0.0002006352,0.0002264899,0.0003356807,0.0002588585,0.0002705645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000730273,"about_ca_system_score_gemma":0.0007103505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03516662,"about_ca_topic_score_gemma":0.03023457,"domain_scores_codex":[0.9997454,0.00003934323,0.00001621646,0.00008898093,0.00004245106,0.00006765834],"domain_scores_gemma":[0.999703,0.00005886788,0.00004591487,0.00002067707,0.000132642,0.0000389257],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001127974,0.0005945306,0.2712909,0.0003027678,0.0003539989,0.0005977587,0.0009105002,0.1267456,0.05067408,0.001627852,0.004317289,0.5414568],"study_design_scores_gemma":[0.00001860441,0.0001337744,0.1632088,0.00001652115,0.0000828673,0.0001018085,0.0004737051,0.8305729,0.00411793,0.0005389883,0.0007029273,0.00003115322],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9233664,0.0003530798,0.07373683,0.00008444888,0.00003775613,0.0000598839,0.0004522941,0.0003996517,0.001509679],"genre_scores_gemma":[0.9814736,0.0000810113,0.01729547,0.00001723344,0.00001412495,0.00002415378,0.0004253151,0.00000790078,0.0006611001],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03516662,"threshold_uncertainty_score":0.06992388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06085233521828026,"score_gpt":0.3311882727294067,"score_spread":0.2703359375111264,"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."}}