{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000925695,0.0003501004,0.0004798389,0.00009812117,0.0002620969,0.0006276064,0.0004655919,0.0000746788,0.0003375515],"category_scores_gemma":[0.0004576894,0.0002786429,0.00005810983,0.000127956,0.0002448239,0.0003053586,0.0001593535,0.0002833224,0.0001367392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007319127,"about_ca_system_score_gemma":0.0001484269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002294963,"about_ca_topic_score_gemma":0.00002910968,"domain_scores_codex":[0.9974021,0.0004994906,0.0002743282,0.0008494393,0.0004316989,0.0005429242],"domain_scores_gemma":[0.9983727,0.0006673984,0.0002690347,0.000376067,0.0001853576,0.000129479],"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":[0.000313923,0.00007218687,0.0007327096,0.0003026901,0.000009522241,0.000004388598,0.001378999,0.05329484,0.9100285,0.03281458,0.0004211777,0.0006265168],"study_design_scores_gemma":[0.001049813,0.001537692,0.00193557,0.0003885806,0.00002383596,0.000005238344,0.0005837473,0.9220647,0.06812631,0.001911748,0.001749645,0.0006231195],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7678927,0.00009756676,0.2206788,0.001081247,0.001845387,0.001926736,0.0004091077,0.0002599793,0.005808495],"genre_scores_gemma":[0.9871849,0.000005672588,0.008588506,0.0002548251,0.00009276839,0.000117937,0.00004499916,0.00003631374,0.003674039],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8687699,"threshold_uncertainty_score":0.9999666,"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."}}