{"id":"W2765498958","doi":"10.1145/3132211.3132457","title":"High speed object tracking using edge computing","year":2017,"lang":"en","type":"article","venue":"","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Enhanced Data Rates for GSM Evolution; Object (grammar); Tracking (education); Computer vision; Video tracking; Artificial intelligence","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":[0.00023864,0.0003714118,0.0002938865,0.0007507781,0.0004972154,0.0009496355,0.0006981273,0.0005271524,0.001331857],"category_scores_gemma":[0.0006536019,0.0001735867,0.0002246394,0.0007500176,0.0002420424,0.001025598,0.0005692478,0.0003388349,0.0003914308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004393886,"about_ca_system_score_gemma":0.0003646723,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005157029,"about_ca_topic_score_gemma":0.007378098,"domain_scores_codex":[0.9997844,0.00003016334,0.00001077187,0.00005407275,0.00007669838,0.00004387355],"domain_scores_gemma":[0.9996887,0.00008503963,0.00002572375,0.00006235509,0.0001135353,0.00002458849],"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":[0.001667379,0.000531539,0.008377378,0.0001401206,0.0001317124,0.0005583054,0.0001967848,0.1850109,0.2013686,0.01140768,0.007201239,0.5834084],"study_design_scores_gemma":[0.00002611888,0.0001237361,0.001803082,0.000009015432,0.00002186243,0.0001120012,0.00002661085,0.9459079,0.04535251,0.002933557,0.003662971,0.00002065543],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2421947,0.000793551,0.7367806,0.0002965276,0.0001790239,0.0001362362,0.000226895,0.003506446,0.01588606],"genre_scores_gemma":[0.7993993,0.0002762772,0.1957484,0.0001580326,0.00003079067,0.00003806066,0.0002248934,0.00006784542,0.004056238],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005157029,"threshold_uncertainty_score":0.01025403,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06602524021494242,"score_gpt":0.3059785572768434,"score_spread":0.2399533170619009,"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."}}