{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006569232,0.0001885214,0.000368623,0.00002309634,0.0004099079,0.00009439615,0.0003210987,0.000009278931,0.0003012412],"category_scores_gemma":[0.0004405369,0.0001451549,0.0001179117,0.0002281413,0.0004225142,0.000267334,0.00004772144,0.0001141809,0.0001730567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003743259,"about_ca_system_score_gemma":0.00001907083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009218444,"about_ca_topic_score_gemma":0.000003758925,"domain_scores_codex":[0.9984449,0.0002624177,0.0002622311,0.0004344825,0.000205741,0.000390199],"domain_scores_gemma":[0.9990752,0.0003001409,0.0001434568,0.0002533666,0.0001338925,0.00009396837],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002663652,0.00009540188,0.000001564251,0.0002333695,0.000005230908,0.000001460639,0.0005947644,0.001322379,0.9931794,0.000808158,0.001722498,0.002009067],"study_design_scores_gemma":[0.002287422,0.002001592,0.0001477484,0.003781454,0.0004149672,0.00002374327,0.0001333289,0.6415929,0.3341967,0.0007890336,0.01321877,0.001412354],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5475828,0.0003171198,0.4434752,0.006866346,0.0005006004,0.0009143717,0.0000410801,0.0001234453,0.0001790015],"genre_scores_gemma":[0.9816906,0.00005149534,0.005203816,0.01184548,0.0009124635,0.0002511502,0.00001218282,0.00002219328,0.00001057302],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6589828,"threshold_uncertainty_score":0.5919245,"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."}}