{"id":"W2336236367","doi":"10.1145/2899381","title":"Eh?Placer","year":2016,"lang":"en","type":"article","venue":"ACM Transactions on Design Automation of Electronic Systems","topic":"VLSI and FPGA Design Techniques","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; University of Calgary","funders":"Chinese University of Hong Kong; University of Hong Kong; Alberta Innovates - Technology Futures","keywords":"Placer mining; Computer science; Variety (cybernetics); Process (computing); Physical design; Focus (optics); Margin (machine learning); Industrial engineering; Geology; Artificial intelligence; Programming language; Integrated circuit; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0003226266,0.000167512,0.0002136596,0.0002424611,0.0000504118,0.00001762973,0.0002329298,0.0001349886,0.0001775432],"category_scores_gemma":[0.00001810234,0.0001291383,0.00007980857,0.0002332979,0.00002656633,0.0002094763,0.000001091228,0.000106529,0.0001262352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002664904,"about_ca_system_score_gemma":0.00004538818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007084663,"about_ca_topic_score_gemma":0.000001599366,"domain_scores_codex":[0.998862,0.0000943292,0.0003636251,0.0001564993,0.0002130076,0.0003104916],"domain_scores_gemma":[0.9991031,0.0002688465,0.00006393427,0.0004652324,0.00005140925,0.00004744648],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007235008,0.0001534942,0.00001400917,0.0002474278,0.0004278286,0.000002491943,0.0002824233,0.09224038,0.500372,0.004180956,0.004446466,0.3975602],"study_design_scores_gemma":[0.00134606,0.0009078667,0.0001083509,0.0006305625,0.00009367491,0.00006805448,0.00006212446,0.1161819,0.8689765,0.003849176,0.00710487,0.0006708182],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001798196,0.0004305294,0.9952655,0.00007556629,0.0002203399,0.0004528294,0.00001002061,0.001223415,0.0005236455],"genre_scores_gemma":[0.9959738,0.0002675251,0.002632149,0.000007613419,0.00002946489,0.0001972133,0.000001194447,0.00003999968,0.0008510462],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9941756,"threshold_uncertainty_score":0.5266106,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01515586658912994,"score_gpt":0.2190371016718792,"score_spread":0.2038812350827493,"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."}}