{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000226683,0.000142856,0.0001555533,0.00006799395,0.0003673359,0.0003511826,0.0002278082,0.00005851808,0.000001747838],"category_scores_gemma":[0.00003933717,0.0001452578,0.00004323673,0.0003452531,0.0000201903,0.0005813775,0.0002962761,0.0001689976,0.0000201397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003184239,"about_ca_system_score_gemma":0.00003561622,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005703526,"about_ca_topic_score_gemma":0.000002755368,"domain_scores_codex":[0.9988148,0.00004497453,0.0002418939,0.0004371848,0.0001502177,0.0003108828],"domain_scores_gemma":[0.999499,0.00006118263,0.0000818208,0.0001366354,0.00009523553,0.0001261909],"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":[0.000002927991,0.00001560211,0.001064017,0.00004953207,0.0000112946,0.000006822948,0.002459564,0.005779079,0.007815182,0.00001045815,0.0002292017,0.9825563],"study_design_scores_gemma":[0.0002088685,0.00003172886,0.0002978861,0.00003680018,0.000006437593,0.00002882011,0.00002894107,0.9879376,0.01061269,0.0005576888,0.00005108514,0.0002014463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3091968,0.00002968111,0.6889836,0.0001955989,0.0007321328,0.00006845138,8.651458e-8,0.0002528933,0.000540785],"genre_scores_gemma":[0.9289672,0.000001996473,0.06905115,0.001042839,0.000915275,5.508435e-7,9.049396e-7,0.0000126198,0.000007512276],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9823549,"threshold_uncertainty_score":0.592344,"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."}}