{"id":"W7122458349","doi":"10.5281/zenodo.18214655","title":"Edge AI for Emergency Communications in University Industry Innovation Zones","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Northern British Columbia","funders":"","keywords":"Stakeholder; Architecture; Enhanced Data Rates for GSM Evolution; Edge device; Telecommunications network; Systems architecture; Communications system","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":[0.0005029845,0.000320623,0.0002658901,0.0003907726,0.0007919027,0.001692773,0.0008801512,0.0007659555,0.00194],"category_scores_gemma":[0.0009881817,0.0001460289,0.0002647392,0.000373327,0.0005400787,0.001398305,0.001524682,0.0008619266,0.0003585658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008357801,"about_ca_system_score_gemma":0.0007229304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001823221,"about_ca_topic_score_gemma":0.003112526,"domain_scores_codex":[0.9996556,0.0001203715,0.00001355653,0.00006639539,0.00006531151,0.00007875848],"domain_scores_gemma":[0.9994361,0.0002436695,0.00006034867,0.0000818632,0.0001122047,0.0000657425],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008451556,0.0003844647,0.006407312,0.000336033,0.000123141,0.0007816896,0.001304259,0.4767497,0.07209992,0.1477073,0.01096276,0.2822983],"study_design_scores_gemma":[0.00001645186,0.0001362188,0.001402982,0.0000234105,0.00003344084,0.000146928,0.0004474672,0.9466908,0.01028521,0.03169256,0.009096428,0.00002813247],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2107291,0.0008150311,0.7535359,0.001251254,0.0001969785,0.0001188172,0.0001085239,0.001183782,0.03206059],"genre_scores_gemma":[0.9509128,0.0001776415,0.04626783,0.000145036,0.00002941248,0.00003287902,0.00005888167,0.00002576907,0.002349883],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00194,"threshold_uncertainty_score":0.006489992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07221616857009865,"score_gpt":0.2770230268551593,"score_spread":0.2048068582850606,"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."}}