{"id":"W2670084885","doi":"10.23977/isspj.2016.11005","title":"An Improved Method of Signal Processing Platform Component Model","year":2016,"lang":"en","type":"article","venue":"Information Systems and Signal Processing Journal","topic":"Embedded Systems Design Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Dataflow; Computer science; Component (thermodynamics); Dataflow architecture; Data flow diagram; Event (particle physics); Complex event processing; Data-flow analysis; Distributed computing; Parallel computing; Real-time computing; Database; Operating system; Process (computing)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005807071,0.001132167,0.0006842288,0.001346009,0.0008112771,0.001445421,0.002080643,0.0008526369,0.01103943],"category_scores_gemma":[0.001104538,0.0005342804,0.00169947,0.001169047,0.0005447963,0.002318033,0.001097139,0.001367913,0.003039151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009463847,"about_ca_system_score_gemma":0.002038866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0111273,"about_ca_topic_score_gemma":0.006453117,"domain_scores_codex":[0.9992564,0.0001144052,0.00005288317,0.0002000107,0.0003241526,0.00005208295],"domain_scores_gemma":[0.9996843,0.00005144843,0.00001900327,0.00005884018,0.0001719545,0.00001444368],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001245123,0.00007253661,0.001199438,0.0005636519,0.0001027115,0.0006204922,0.000287266,0.446947,0.02446076,0.2634755,0.009851399,0.2522948],"study_design_scores_gemma":[0.000026726,0.00003197784,0.0001600085,0.00002194033,0.00003740828,0.0001763402,0.00002776691,0.9488133,0.003485504,0.02483409,0.02235779,0.00002713267],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0008248921,0.0001313363,0.9954044,0.00005375039,0.00005555406,0.0000487394,0.00007982539,0.0004488549,0.002952721],"genre_scores_gemma":[0.1470968,0.001125916,0.8247143,0.0001348089,0.0001492174,0.000650736,0.001153929,0.0006563288,0.02431806],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0111273,"threshold_uncertainty_score":0.03693062,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03085579351461787,"score_gpt":0.293217143603734,"score_spread":0.2623613500891162,"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."}}