{"id":"W2799261665","doi":"10.1103/physrevlett.120.176401","title":"Discriminative Cooperative Networks for Detecting Phase Transitions","year":2018,"lang":"en","type":"article","venue":"Physical Review Letters","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":114,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"H2020 European Research Council; Canada First Research Excellence Fund; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Discriminative model; Computer science; Scheme (mathematics); Task (project management); Artificial intelligence; Phase (matter); Parameter space; Machine learning; Space (punctuation); Pattern recognition (psychology); Physics; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001094141,0.0005300738,0.0004017065,0.0009764764,0.0004109561,0.0005666375,0.001234219,0.0009636901,0.0008513319],"category_scores_gemma":[0.003519588,0.0003599044,0.0003064589,0.0005321854,0.001195115,0.001562746,0.001471912,0.001006566,0.0002404236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006860145,"about_ca_system_score_gemma":0.0003426407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001150853,"about_ca_topic_score_gemma":0.001523655,"domain_scores_codex":[0.9996409,0.0001146392,0.00001390595,0.0001180326,0.00007979711,0.00003260833],"domain_scores_gemma":[0.9989203,0.000495868,0.0001885549,0.0001816864,0.0001437011,0.00006994447],"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":[0.0004130542,0.0001807378,0.004667657,0.0002214389,0.00007056052,0.0002009098,0.0004240501,0.5575903,0.05383706,0.1489895,0.002558807,0.2308459],"study_design_scores_gemma":[0.000006667482,0.00002568905,0.0002746738,0.000004755015,0.000005665211,0.00002666494,0.00001317802,0.9664964,0.004122787,0.02836534,0.0006506117,0.000007609333],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05390526,0.0002624181,0.9429834,0.000183394,0.00002448861,0.00004229183,0.00005389964,0.0003609375,0.002183976],"genre_scores_gemma":[0.7873807,0.0002178515,0.2092184,0.0001417216,0.00003714601,0.0001292471,0.0001932377,0.00006748684,0.002614143],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001234219,"threshold_uncertainty_score":0.005786479,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02739341881061162,"score_gpt":0.3624572207973755,"score_spread":0.3350638019867638,"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."}}