{"id":"W2280035019","doi":"10.11591/ijres.v4.i2.pp99-121","title":"An Efficient Framework for Floor-plan Prediction of Dynamic Runtime Reconfigurable Systems","year":2015,"lang":"en","type":"article","venue":"International Journal of Reconfigurable and Embedded Systems (IJRES)","topic":"Embedded Systems Design Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Control reconfiguration; Computer science; Field-programmable gate array; Reconfigurable computing; Scheduling (production processes); Embedded system; Computer architecture; Distributed computing; Real-time computing; Engineering","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":[0.0007433865,0.001288456,0.001374803,0.001280005,0.0005175772,0.001298228,0.002186935,0.00103889,0.003859714],"category_scores_gemma":[0.002797841,0.0006994645,0.001260752,0.0009409465,0.0006060656,0.001012974,0.001344997,0.001521641,0.0008765578],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001307454,"about_ca_system_score_gemma":0.00189301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01394598,"about_ca_topic_score_gemma":0.01363031,"domain_scores_codex":[0.9994267,0.0001026812,0.00002987174,0.0001349325,0.0002077302,0.00009801023],"domain_scores_gemma":[0.999244,0.0003412494,0.00008691289,0.00009710751,0.0001730529,0.0000576282],"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":[0.00003796511,0.00003134002,0.0004732645,0.00004387851,0.00001951741,0.00005181669,0.00001775011,0.9550903,0.001390469,0.006107032,0.001070673,0.03566592],"study_design_scores_gemma":[0.000001261852,0.000002634565,0.00002858165,0.000001543028,0.000001087283,0.000002711645,0.000001566053,0.9988581,0.0001108522,0.000836659,0.0001537284,0.000001330956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004413691,0.0001880608,0.9923986,0.00006578322,0.00002987089,0.00003859208,0.0001461194,0.001646063,0.001073247],"genre_scores_gemma":[0.384608,0.0004258799,0.6104136,0.0001117822,0.0001150295,0.0002981775,0.0009583609,0.0005087378,0.002560433],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01394598,"threshold_uncertainty_score":0.02772963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03600877895987122,"score_gpt":0.296907898462213,"score_spread":0.2608991195023418,"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."}}