{"id":"W3214414631","doi":"10.48550/arxiv.2107.03330","title":"Machine-learning approach to finite-size effects in systems with strongly interacting fermions","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Quantum, superfluid, helium dynamics","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Spurious relationship; Context (archaeology); Statistical physics; Thermodynamic limit; Limit (mathematics); Diffusion Monte Carlo; Computer science; Artificial neural network; Classification of discontinuities; Fermion; Physics; Monte Carlo method; Variety (cybernetics); Range (aeronautics); Machine learning; Artificial intelligence; Mathematics; Quantum mechanics; Hybrid Monte Carlo; Markov chain Monte Carlo","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":[0.001730688,0.0004698775,0.0007770989,0.000907813,0.0008376031,0.0008721224,0.002020919,0.001213137,0.001663369],"category_scores_gemma":[0.00493952,0.0002490392,0.0006105138,0.0005644533,0.002161825,0.001755432,0.001002638,0.001638024,0.0001331344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001260094,"about_ca_system_score_gemma":0.000890323,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003296155,"about_ca_topic_score_gemma":0.003146088,"domain_scores_codex":[0.9995874,0.0002414379,0.00001123675,0.00002956674,0.00008924015,0.00004118031],"domain_scores_gemma":[0.996899,0.002395215,0.0002162533,0.0002080686,0.0001770876,0.0001043172],"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":[0.00001897126,0.00005110383,0.0006708641,0.00007202646,0.00002626664,0.00009474979,0.00007513281,0.7279812,0.001009848,0.2655761,0.0003502136,0.004073635],"study_design_scores_gemma":[0.000001845164,0.000002730726,0.00004577457,0.000002434966,8.235503e-7,0.000004188392,0.000002449661,0.9678057,0.00008839551,0.031971,0.00007238898,0.000002144907],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.164002,0.0009180026,0.819281,0.003133446,0.0001323894,0.00006047701,0.0000932612,0.0002624498,0.01211702],"genre_scores_gemma":[0.8819948,0.0005706598,0.1116975,0.0003231326,0.0002107189,0.0001493296,0.00006971924,0.0001001072,0.004884033],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003296155,"threshold_uncertainty_score":0.009152889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02688384326530327,"score_gpt":0.1815944274369089,"score_spread":0.1547105841716056,"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."}}