{"id":"W3090597981","doi":"10.22360/springsim.2020.cns.003","title":"Scalable Object Detection, Tracking and Pattern Recognition Model Using Edge Computing","year":2020,"lang":"en","type":"article","venue":"","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Scalability; Artificial intelligence; Computer vision; Cognitive neuroscience of visual object recognition; Enhanced Data Rates for GSM Evolution; Object detection; Pattern recognition (psychology); Edge computing; Video tracking; Object (grammar); Database","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003065299,0.0007167113,0.0007912929,0.000508653,0.0005222786,0.0007883486,0.001284493,0.0007059009,0.001101115],"category_scores_gemma":[0.0005413071,0.0002454766,0.0008012479,0.0007574726,0.000337766,0.001580737,0.0005239502,0.000716469,0.0003941309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006474066,"about_ca_system_score_gemma":0.0008279864,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007290775,"about_ca_topic_score_gemma":0.004488489,"domain_scores_codex":[0.9996827,0.00002626459,0.00001370791,0.0001218715,0.0001064502,0.00004910064],"domain_scores_gemma":[0.999799,0.0000415482,0.00002256301,0.00004674108,0.00006975934,0.00002058341],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000396702,0.0003637675,0.003546939,0.0001254075,0.000176024,0.0005871702,0.0001321558,0.5974781,0.04572916,0.02538197,0.008186038,0.3178966],"study_design_scores_gemma":[0.000004427373,0.00001701987,0.0001863223,0.000001387342,0.000007389494,0.00003826768,0.000005433017,0.9955291,0.001934214,0.001800244,0.0004710686,0.000005148519],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02734954,0.0002845867,0.9685209,0.0002070877,0.00007925564,0.00007844396,0.00007625213,0.0009640463,0.002439856],"genre_scores_gemma":[0.6606952,0.0006085009,0.3299046,0.0002398716,0.0001073744,0.0001917878,0.0004494963,0.00009413934,0.007709008],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007290775,"threshold_uncertainty_score":0.01449668,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07367232786506846,"score_gpt":0.2553973605387089,"score_spread":0.1817250326736405,"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."}}