{"id":"W4230054345","doi":"10.32920/ryerson.14646711","title":"Obstacle detection using Microsoft Kinect","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"RANSAC; Computer science; Computer vision; Artificial intelligence; Segmentation; Obstacle; Point cloud; Software; Point (geometry); MATLAB; Image segmentation; Computer graphics (images); Image (mathematics); 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":[0.000579498,0.0002203339,0.0003031292,0.0001121241,0.0001109052,0.0007039209,0.0007294947,0.000230129,0.00002243537],"category_scores_gemma":[0.0001098182,0.0002194675,0.0001933494,0.0003100658,0.00002035437,0.0002321149,0.001382465,0.0004631896,0.00001154975],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009387206,"about_ca_system_score_gemma":0.0001946907,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000473641,"about_ca_topic_score_gemma":0.0001534417,"domain_scores_codex":[0.998245,0.0002389409,0.0002654037,0.0007480842,0.000225094,0.0002774538],"domain_scores_gemma":[0.9984453,0.0001256222,0.0001388593,0.001060279,0.0001659028,0.00006404761],"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":[0.000008877402,0.0001718624,0.007230507,0.0004167707,0.0002335998,0.0002723157,0.001231496,0.02038195,0.2119668,0.000970286,0.00007062289,0.7570449],"study_design_scores_gemma":[0.0004359696,0.00005026479,0.03283735,0.0003153999,0.00003804445,0.0003087436,0.00006017589,0.4335318,0.5215878,0.007433073,0.001950823,0.001450499],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2244784,0.0003621806,0.7718387,0.00007414701,0.002240178,0.0000889197,9.589054e-7,0.0002920985,0.0006244472],"genre_scores_gemma":[0.6016052,0.00001701529,0.397992,0.0001549304,0.0001215932,0.000004863157,0.00000309353,0.00001212995,0.0000891567],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7555944,"threshold_uncertainty_score":0.8949624,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05824360816384602,"score_gpt":0.322409238116959,"score_spread":0.264165629953113,"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."}}