{"id":"W4210768133","doi":"10.14569/ijacsa.2022.0130175","title":"Balanced Schedule on Storm for Performance Enhancement","year":2022,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Academy of Scientific Research and Technology","keywords":"Computer science; Workload; Scheduling (production processes); Distributed computing; Storm; Big data; Schedule; Latency (audio); Network topology; Real-time computing; Computer network; Operating system; Telecommunications; Mathematical optimization","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":[0.0007979703,0.0006824635,0.0007522814,0.0006953186,0.0008460924,0.0007165806,0.0009499365,0.0002877646,0.006315318],"category_scores_gemma":[0.002022747,0.0002305665,0.0004245538,0.0008364015,0.0003643222,0.0009695163,0.0008147808,0.0005469942,0.001018095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001076112,"about_ca_system_score_gemma":0.002324363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004638216,"about_ca_topic_score_gemma":0.005502438,"domain_scores_codex":[0.9993508,0.0001534548,0.00003181973,0.0001081333,0.0001816877,0.0001741312],"domain_scores_gemma":[0.9991161,0.0002863519,0.00007654163,0.000127817,0.0002687339,0.0001243794],"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":[0.002735343,0.0003651117,0.001916861,0.000457171,0.0001361062,0.0003992118,0.0003193174,0.6607466,0.03763743,0.04974272,0.04124863,0.2042954],"study_design_scores_gemma":[0.0001766945,0.0004297056,0.0006523208,0.00001478074,0.00003044138,0.0001538546,0.00009257541,0.96529,0.006140778,0.01355023,0.01344303,0.00002571094],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1663119,0.001627092,0.7966806,0.0009309623,0.0007158946,0.0005916349,0.0009055009,0.006168659,0.02606778],"genre_scores_gemma":[0.8357559,0.0006368925,0.1536365,0.0002384309,0.0001709756,0.0002619526,0.0008815129,0.0004151739,0.008002585],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006315318,"threshold_uncertainty_score":0.02112687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00971491653426371,"score_gpt":0.2671384462725287,"score_spread":0.257423529738265,"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."}}