{"id":"W4405519226","doi":"10.2478/cait-2024-0036","title":"A Cost-Benefit Model for Feasible IoT Edge Resources Scalability to Improve Real-Time Processing Performance","year":2024,"lang":"en","type":"article","venue":"Cybernetics and Information Technologies","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Seneca Polytechnic","funders":"","keywords":"Computer science; Scalability; Enhanced Data Rates for GSM Evolution; Internet of Things; Distributed computing; Real-time computing; Embedded system; Artificial intelligence; 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":[0.0003227745,0.0001525048,0.0001485482,0.0002627485,0.0002386034,0.0008355139,0.0004948997,0.0001349953,2.735842e-7],"category_scores_gemma":[0.00009968497,0.0001303991,0.00003536764,0.0004526965,0.00007734268,0.001314615,0.000490071,0.0001310901,0.00002716481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006286879,"about_ca_system_score_gemma":0.00006367402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004537526,"about_ca_topic_score_gemma":3.228752e-7,"domain_scores_codex":[0.9989659,0.000003204527,0.000326944,0.0002424482,0.0001505786,0.0003109473],"domain_scores_gemma":[0.9993442,0.00005425994,0.00007435233,0.0003152259,0.0001674431,0.00004450926],"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":[0.000006030033,0.000006844698,0.00005911077,0.0002864043,0.000004103326,1.049697e-7,0.002678543,0.001100906,0.0002558493,0.004959996,0.001550383,0.9890917],"study_design_scores_gemma":[0.000103029,0.0001130238,0.0002289173,0.0001129033,0.000004351889,0.000003147728,0.0001027804,0.9669139,0.004297368,0.005597888,0.0223411,0.000181573],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4792987,0.0003846386,0.5100486,0.002170108,0.0007742064,0.0009829216,0.000005503134,0.002725699,0.003609629],"genre_scores_gemma":[0.8634081,0.0002020529,0.1353051,0.0001539191,0.0001289024,0.0001863292,0.00000767401,0.00001417729,0.0005936956],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9889101,"threshold_uncertainty_score":0.805688,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01756012922285051,"score_gpt":0.2489134301608249,"score_spread":0.2313533009379744,"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."}}