{"id":"W4254562804","doi":"10.32920/ryerson.14665893","title":"SoC for real - time object tracking in 3D space","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Alertness; Object (grammar); Computer science; Tracking (education); Video tracking; Warning system; Space (punctuation); Hazardous waste; Work (physics); Simulation; Human–computer interaction; Computer vision; Computer security; Real-time computing; Aeronautics; Artificial intelligence; Engineering; Psychology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001969219,0.0002905738,0.0005727192,0.0001835327,0.0000672097,0.0005728614,0.001084511,0.0003208368,0.00004141075],"category_scores_gemma":[0.0002298866,0.0002881709,0.0002501894,0.0003073813,0.00002477593,0.0002664782,0.001010193,0.0004883982,0.00002045055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001044207,"about_ca_system_score_gemma":0.0004364664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004564149,"about_ca_topic_score_gemma":0.0003968501,"domain_scores_codex":[0.9975576,0.0002986629,0.0003985663,0.001007123,0.0002701512,0.0004679138],"domain_scores_gemma":[0.997882,0.0006662515,0.0001642897,0.001066796,0.0001498597,0.00007079425],"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":[0.00005639461,0.0005172354,0.01507242,0.001185757,0.0003161421,0.0004866832,0.008112821,0.01551441,0.005723725,0.02307799,0.003540208,0.9263962],"study_design_scores_gemma":[0.00283466,0.0002777214,0.116233,0.001671623,0.00006210484,0.00008613719,0.0002388843,0.7907327,0.02254111,0.05482272,0.006668572,0.003830738],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01577164,0.0002117051,0.9671836,0.0009898954,0.00115126,0.0004573351,0.000005882662,0.000358824,0.01386989],"genre_scores_gemma":[0.101849,0.0001319529,0.8958823,0.000189309,0.0002462934,0.0000874984,0.0000317143,0.00003205545,0.0015499],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9225655,"threshold_uncertainty_score":0.999957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04381131404775677,"score_gpt":0.3398763593293146,"score_spread":0.2960650452815579,"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."}}