{"id":"W4404103226","doi":"10.1109/mlcad62225.2024.10740240","title":"Enabling Risk Management of Machine Learning Predictions for FPGA Routability","year":2024,"lang":"en","type":"article","venue":"","topic":"Physical Unclonable Functions (PUFs) and Hardware Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Field-programmable gate array; Computer science; Embedded system; Parallel computing; Computer architecture; Machine learning; 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":[0.005164445,0.001320358,0.0006864971,0.00130158,0.0005397623,0.002102206,0.001692377,0.00130462,0.002296614],"category_scores_gemma":[0.04159445,0.000470478,0.0006225291,0.0005893043,0.0005630358,0.002717267,0.001955115,0.002487437,0.0008055281],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001374532,"about_ca_system_score_gemma":0.001398193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003554709,"about_ca_topic_score_gemma":0.004566072,"domain_scores_codex":[0.9970466,0.001003914,0.000188822,0.0006226038,0.0009629547,0.0001749564],"domain_scores_gemma":[0.9696733,0.01971498,0.003392627,0.003447954,0.003326123,0.0004451014],"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":[0.0005915854,0.0003168253,0.02583884,0.0001130315,0.0001417127,0.0002271233,0.0003434591,0.7041927,0.006355155,0.006517376,0.003671094,0.2516911],"study_design_scores_gemma":[0.000007372013,0.00003942665,0.0006784516,0.00001335773,0.000009763975,0.00003060838,0.00001427928,0.9920315,0.003190104,0.003543664,0.0004314269,0.00001018339],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1129199,0.0004128442,0.874266,0.0008965187,0.00007120391,0.0001382157,0.0002648353,0.007594182,0.003436261],"genre_scores_gemma":[0.8414665,0.0001269866,0.1561153,0.0002520599,0.00006768572,0.0001076021,0.0002921836,0.0002452363,0.001326482],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005164445,"threshold_uncertainty_score":0.02731252,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01436761228365385,"score_gpt":0.2495621753023648,"score_spread":0.2351945630187109,"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."}}