{"id":"W2981125068","doi":"10.5194/isprs-archives-xlii-4-w18-25-2019","title":"URBAN VISION DEVELOPMENT IN ORDER TO MONITOR WHEELCHAIR USERS BASED ON THE YOLO ALGORITHM","year":2019,"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":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Wheelchair; Population; Intersection (aeronautics); Urbanization; Computer science; Order (exchange); Sample (material); Artificial intelligence; Object (grammar); Algorithm; Machine learning; Transport engineering; Business; Sociology; Engineering; Demography; World Wide Web; Economic growth","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003333817,0.0004046822,0.0004000634,0.001729651,0.000313519,0.0006236605,0.0003883344,0.0003724507,0.001681747],"category_scores_gemma":[0.0006805171,0.0001400456,0.0003789228,0.0007176272,0.0001703772,0.0003322078,0.0003490481,0.0002448609,0.0005135611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005555072,"about_ca_system_score_gemma":0.0007969327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03315104,"about_ca_topic_score_gemma":0.04366249,"domain_scores_codex":[0.9998406,0.00001638817,0.000008874139,0.00004530965,0.0000390686,0.00004969448],"domain_scores_gemma":[0.9998395,0.00002108805,0.00001671959,0.00000848939,0.0001011932,0.00001295451],"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":[0.0009084966,0.0005886727,0.1091055,0.0002406562,0.0002436475,0.0002875376,0.0002771466,0.108516,0.04396224,0.002210363,0.009371607,0.7242881],"study_design_scores_gemma":[0.0000240312,0.00009826469,0.04834133,0.00002539116,0.00005115004,0.0001013434,0.0001458703,0.9422497,0.007190478,0.0002415609,0.001513884,0.00001693353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7093216,0.0005510649,0.2780761,0.0003168308,0.0001021873,0.0002656013,0.001054739,0.001966621,0.00834527],"genre_scores_gemma":[0.8750549,0.0002144566,0.1200106,0.00009767062,0.00001629787,0.0001398944,0.00130625,0.00005130121,0.003108654],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03315104,"threshold_uncertainty_score":0.06591612,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0126165846741924,"score_gpt":0.2634452626019402,"score_spread":0.2508286779277478,"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."}}