{"id":"W4402473563","doi":"10.1109/ccece59415.2024.10667322","title":"Predictive Resource Usage Characterization for Extreme Edge Computing","year":2024,"lang":"en","type":"article","venue":"","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Characterization (materials science); Enhanced Data Rates for GSM Evolution; Resource (disambiguation); Edge computing; Distributed computing; Artificial intelligence; Computer network; Materials science; Nanotechnology","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":[0.0006114166,0.0005175627,0.0005004579,0.0005883292,0.0004461998,0.0008620124,0.001009887,0.0003468342,0.0008278101],"category_scores_gemma":[0.003223878,0.0001910363,0.0002314517,0.0007588989,0.0005318583,0.00111201,0.000708682,0.0008284943,0.0001850317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005849143,"about_ca_system_score_gemma":0.0005654409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00262281,"about_ca_topic_score_gemma":0.001928442,"domain_scores_codex":[0.9994153,0.00009842274,0.00002458551,0.0001299811,0.000246058,0.00008571275],"domain_scores_gemma":[0.9988979,0.000446398,0.0001352558,0.0002628186,0.0001922002,0.00006539009],"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":[0.0003081801,0.0001474641,0.009329408,0.0000628998,0.00003224929,0.0001549033,0.0001462276,0.8891299,0.02786951,0.01625897,0.001510042,0.05505026],"study_design_scores_gemma":[0.00000164921,0.00001831516,0.0009828841,0.000002808915,0.000001892967,0.00002306418,0.00002100286,0.992855,0.002782039,0.002987861,0.0003165914,0.0000068566],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1843322,0.0002021408,0.810381,0.0001616086,0.0000411564,0.00006832882,0.0001942364,0.000694768,0.003924539],"genre_scores_gemma":[0.9700979,0.00006357423,0.02911095,0.00003325339,0.00001545826,0.00003740095,0.0001715502,0.00005356397,0.0004162851],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00262281,"threshold_uncertainty_score":0.005215049,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02478829931847476,"score_gpt":0.241924854757431,"score_spread":0.2171365554389562,"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."}}