{"id":"W4402193695","doi":"10.1109/fccm60383.2024.00026","title":"Mapping Enumeration for Multi-Context CGRAs Using Zero-Suppressed Binary Decision Diagrams","year":2024,"lang":"en","type":"article","venue":"","topic":"Digital Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Enumeration; Binary decision diagram; Computer science; Context (archaeology); Zero (linguistics); Binary number; Influence diagram; Theoretical computer science; Parallel computing; Programming language; Mathematics; Discrete mathematics; Arithmetic; Decision tree; Data mining","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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0002788233,0.0001571656,0.0001348685,0.0002242975,0.0001365325,0.002025658,0.0005553553,0.00006906931,0.000003960827],"category_scores_gemma":[0.0001310131,0.0001353178,0.00009098789,0.000457937,0.0000323789,0.002743716,0.0002706889,0.00008229266,0.00002452437],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006209376,"about_ca_system_score_gemma":0.00007616702,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001960127,"about_ca_topic_score_gemma":0.000003968843,"domain_scores_codex":[0.9987894,0.00001717228,0.0002621861,0.0004926674,0.0001918444,0.0002467599],"domain_scores_gemma":[0.9992357,0.0001946922,0.00004177297,0.0003436931,0.0001241142,0.0000599682],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004609265,0.00006994633,0.00003323084,0.00009056502,0.00001291507,0.00001756753,0.0004963336,0.00004527449,0.04663862,0.01112878,0.003550647,0.9379115],"study_design_scores_gemma":[0.0001390039,0.00004499485,0.00003641196,0.0002768054,0.000003586009,0.00001342075,0.00001795875,0.9240482,0.04708581,0.02391605,0.004226039,0.0001917088],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008069842,0.0006529812,0.9883988,0.0002169168,0.000428585,0.0003425514,0.000002804946,0.001556917,0.0003305461],"genre_scores_gemma":[0.452094,0.000003960471,0.5474182,0.0001499146,0.00002549055,0.00003107817,0.000003629231,0.00001364347,0.0002600253],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9377198,"threshold_uncertainty_score":0.9990103,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07944118068427401,"score_gpt":0.340843524690577,"score_spread":0.261402344006303,"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."}}