{"id":"W4387682487","doi":"10.2139/ssrn.4603485","title":"Towards Accurate Thermodynamics from Random Energy Sampling","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Statistical physics; Thermodynamics; Sampling (signal processing); Energy (signal processing); Computer science; Physics; Econometrics; Mathematics; Statistics","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","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.007519489,0.0006454654,0.000889457,0.0002639312,0.0005572771,0.001216919,0.002776211,0.0004629717,0.0006126079],"category_scores_gemma":[0.0007071114,0.0005502296,0.0003482859,0.0002323666,0.0001820239,0.0002763473,0.001567403,0.004324504,0.0003929104],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001601438,"about_ca_system_score_gemma":0.005746895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005534597,"about_ca_topic_score_gemma":0.0022332,"domain_scores_codex":[0.9921023,0.0008141455,0.001021713,0.001100081,0.001075636,0.003886098],"domain_scores_gemma":[0.9971732,0.0003436235,0.001174257,0.0008904965,0.0002124638,0.0002059331],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001009892,0.0001606598,0.0004406733,0.0001125988,0.0005275795,0.00009032157,0.001275655,0.3736835,0.5039368,0.09582851,0.0002834936,0.02265028],"study_design_scores_gemma":[0.001053836,0.0001107125,0.0004994291,0.0002259344,0.0001132496,0.0001312071,0.0003004895,0.03586859,0.002524449,0.9576951,0.0006383493,0.0008386665],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4925233,0.001439264,0.4963381,0.0009970432,0.007446666,0.0001983671,0.0001370813,0.000465691,0.0004544496],"genre_scores_gemma":[0.9813884,0.007106008,0.006357712,0.0001997292,0.00283847,0.00005634138,0.0001371671,0.0002096713,0.001706424],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8618666,"threshold_uncertainty_score":0.9998896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02399971092342679,"score_gpt":0.2924407702426331,"score_spread":0.2684410593192063,"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."}}