{"id":"W3214045974","doi":"10.1109/biomdlore49470.2021.9594328","title":"Detection of Wheelchair Orientation in Human-Robot Interactions","year":2021,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Wheelchair; Computer science; Artificial intelligence; Orientation (vector space); Robot; Mobile robot; Computer vision; Cluster analysis; Laser scanning; Object detection; Classifier (UML); Pattern recognition (psychology); Laser; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005876843,0.00003678791,0.00006318017,0.0001261903,0.00003183172,0.00001422219,0.0001135169,0.00002587835,0.00001481099],"category_scores_gemma":[0.00002926559,0.00003734375,0.00002152617,0.0004397802,0.00001760381,0.000168314,0.00005201959,0.00008263616,0.00000784589],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000288435,"about_ca_system_score_gemma":0.00001649265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007314824,"about_ca_topic_score_gemma":0.0007387091,"domain_scores_codex":[0.9995609,0.00002710245,0.000130582,0.000152313,0.00005765681,0.00007147127],"domain_scores_gemma":[0.9996856,0.00002739056,0.0000418254,0.0001805822,0.0000544037,0.00001022776],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000001157704,0.0001344487,0.004948821,0.000005887424,0.000005880598,0.00001118536,0.0002356603,0.0001079815,0.8267401,0.07249625,0.00002753015,0.09528503],"study_design_scores_gemma":[0.0001851397,0.00004041226,0.1052212,0.00001566063,0.000001643753,0.00001720296,0.0001819154,0.004298977,0.8844418,0.00507725,0.0004559864,0.00006278913],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2987134,0.000008817498,0.6983065,0.0003295999,0.0001784857,0.00002042483,1.247284e-7,0.00009444304,0.002348254],"genre_scores_gemma":[0.9894904,0.000001304977,0.0101172,0.00003163511,0.000006836892,0.000005090362,9.811895e-7,0.000001699931,0.0003448552],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.690777,"threshold_uncertainty_score":0.1522834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02240844823969601,"score_gpt":0.2978188275773732,"score_spread":0.2754103793376771,"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."}}