{"id":"W1989828912","doi":"10.1109/mdt.2010.117","title":"Automatic TLM Generation for Early Validation of Multicore Systems","year":2010,"lang":"en","type":"article","venue":"IEEE Design & Test of Computers","topic":"Embedded Systems Design Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Multi-core processor; Computer science; Leverage (statistics); Software; Computer architecture; Task (project management); Transaction-level modeling; Embedded system; Software engineering; Parallel computing; Programming language; SystemC; Engineering; Systems engineering; Artificial intelligence","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":[0.003686276,0.001130368,0.0007406884,0.001486379,0.0007362748,0.001286423,0.002758769,0.001430145,0.004983088],"category_scores_gemma":[0.01418426,0.0009357107,0.001475399,0.0005334314,0.001068232,0.002669476,0.002404957,0.002259269,0.00129522],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00118597,"about_ca_system_score_gemma":0.00186556,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001807258,"about_ca_topic_score_gemma":0.002990982,"domain_scores_codex":[0.9929098,0.002640933,0.000519375,0.0006523498,0.002777498,0.0004999441],"domain_scores_gemma":[0.986912,0.00590309,0.0008623054,0.004008458,0.002190814,0.0001233788],"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":[0.0009979182,0.000308106,0.005519241,0.0009245427,0.000184261,0.001342163,0.0009131612,0.380655,0.1713921,0.04951757,0.0105323,0.3777137],"study_design_scores_gemma":[0.00006655103,0.0001565907,0.0002916424,0.00005826508,0.00003294285,0.0002041484,0.00004536006,0.8678916,0.1084569,0.01458958,0.008167981,0.00003842411],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01813963,0.00009705788,0.9666558,0.0001314317,0.00004843011,0.0001336553,0.000180367,0.013335,0.001278711],"genre_scores_gemma":[0.4337293,0.0001116855,0.5582914,0.0002558483,0.00002995077,0.0003722831,0.001185333,0.002975347,0.003048849],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004983088,"threshold_uncertainty_score":0.01949513,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04656795164786921,"score_gpt":0.2737313232756444,"score_spread":0.2271633716277752,"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."}}