{"id":"W2971953930","doi":"","title":"Discriminative and generative machine learning for spin systems based on physically interpretable features","year":2019,"lang":"en","type":"article","venue":"StatPhys 27 Main Conference","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Ontario Tech University","funders":"","keywords":"Artificial intelligence; Discriminative model; Artificial neural network; Computer science; Physical system; A priori and a posteriori; Representation (politics); Generative grammar; Machine learning; Physics","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":[0.0007389357,0.0004206089,0.0007353479,0.0008311554,0.0003352073,0.0009183322,0.001011899,0.0008833458,0.001929113],"category_scores_gemma":[0.002916687,0.0005501227,0.0008131759,0.0005495735,0.001863949,0.00146487,0.001150003,0.002155768,0.0003023271],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001043968,"about_ca_system_score_gemma":0.0004363529,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001941322,"about_ca_topic_score_gemma":0.003451343,"domain_scores_codex":[0.9997965,0.0000695106,0.000007567047,0.00005373648,0.0000443313,0.00002844293],"domain_scores_gemma":[0.9987526,0.0008298546,0.0001183664,0.0001654743,0.00007199357,0.00006165317],"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":[0.00008326023,0.00005487893,0.002383354,0.0001617063,0.00005815861,0.000103912,0.0001697829,0.7867113,0.004817839,0.164857,0.001756554,0.03884217],"study_design_scores_gemma":[0.000002866308,0.000006168111,0.0002012482,0.00000639524,0.000002535797,0.00001193359,0.000005135413,0.9516309,0.0002490256,0.0476704,0.0002096112,0.00000382012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1287781,0.001282228,0.8621143,0.001280947,0.00008016605,0.00003978974,0.0002767067,0.0008705703,0.00527729],"genre_scores_gemma":[0.9247912,0.0004797677,0.07108922,0.0002286772,0.00008801196,0.00005645404,0.0004159284,0.0001535826,0.002697254],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001941322,"threshold_uncertainty_score":0.007574618,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01348743648171767,"score_gpt":0.2741434907806536,"score_spread":0.2606560542989359,"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."}}