{"id":"W2167732087","doi":"10.1109/icorr.2005.1501078","title":"The Laser Line Object Detection Method in an Anti-Collision System for Powered Wheelchair","year":2005,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Rehabilitation Institute","funders":"","keywords":"Wheelchair; Collision; Line (geometry); Computer science; Object (grammar); Object detection; Computer vision; Simulation; Artificial intelligence; Pattern recognition (psychology); Computer security; 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":[0.0008940768,0.0001011599,0.0001384211,0.0001252249,0.0001988938,0.0001048996,0.0005153174,0.00009721552,6.811017e-7],"category_scores_gemma":[0.00005676968,0.0000654669,0.00004735313,0.0003450911,0.00002080548,0.0002457212,0.00007301524,0.0001174285,0.00001263916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008385946,"about_ca_system_score_gemma":0.00002237761,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004024703,"about_ca_topic_score_gemma":0.0009388004,"domain_scores_codex":[0.9989941,0.0001129649,0.0002146386,0.0003230456,0.0001087398,0.0002465696],"domain_scores_gemma":[0.9991574,0.0002184089,0.00006818694,0.0004384219,0.00008475204,0.00003283722],"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":[0.00003537475,0.0001408651,0.0003918845,0.00002021166,0.00001257981,0.000005165922,0.0001940678,0.002867967,0.02421243,0.03241742,0.0001077794,0.9395943],"study_design_scores_gemma":[0.0006684282,0.0003126914,0.00534664,0.00002556079,0.000004060931,0.00002181915,0.0001909343,0.6443428,0.3432777,0.000612661,0.005037383,0.0001592751],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1471619,0.00003522937,0.8503609,0.001254081,0.0002472094,0.0002329339,0.00000100076,0.0004783889,0.000228319],"genre_scores_gemma":[0.8774982,0.000003195143,0.122183,0.00005216866,0.00005461202,0.00003802268,6.244858e-7,0.000006521405,0.0001635848],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.939435,"threshold_uncertainty_score":0.2669663,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01745306483516487,"score_gpt":0.2975543212558434,"score_spread":0.2801012564206786,"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."}}