{"id":"W4281724430","doi":"10.3390/condmat7020038","title":"Neural Annealing and Visualization of Autoregressive Neural Networks in the Newman–Moore Model","year":2022,"lang":"en","type":"article","venue":"Condensed Matter","topic":"Theoretical and Computational Physics","field":"Physics and Astronomy","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of Waterloo; Perimeter Institute","funders":"","keywords":"Artificial neural network; Statistical physics; Quantum entanglement; Degenerate energy levels; Frustration; Autoregressive model; Chaotic; Computer science; Fractal; Simulated annealing; Artificial intelligence; Quantum; Theoretical computer science; Theoretical physics; Physics; Mathematics; Algorithm; Quantum mechanics; Condensed matter physics; Mathematical analysis; Econometrics","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":[],"consensus_categories":[],"category_scores_codex":[0.0000599713,0.00006797678,0.00008667947,0.00001997934,0.00008602395,0.0000212735,0.00009557028,0.000008910845,0.0001714534],"category_scores_gemma":[7.103108e-7,0.000052821,0.00003169624,0.00007224028,0.00005448405,0.00004656543,0.00007345158,0.0001157148,9.816647e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004499109,"about_ca_system_score_gemma":0.000008166717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001329591,"about_ca_topic_score_gemma":2.20765e-7,"domain_scores_codex":[0.9994894,0.00007463509,0.0001229773,0.0001061361,0.000105934,0.0001009403],"domain_scores_gemma":[0.9997525,0.00007480228,0.00006010557,0.00007578137,0.00002101812,0.0000157903],"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":[0.00002763331,0.00004410121,0.01554306,0.000006419938,0.000009634197,0.000001561924,0.001189829,0.8173993,0.00009513771,0.1636161,0.0008820581,0.00118526],"study_design_scores_gemma":[0.0002421681,0.00001695425,0.00472721,0.0000029411,0.000008278116,0.000001025213,0.0003067017,0.9140338,0.00002161748,0.08056796,0.00001145495,0.0000598663],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9771871,0.00003011357,0.02127233,0.0005934048,0.00005089769,0.0001056252,0.00001588559,0.000005998036,0.0007386694],"genre_scores_gemma":[0.998773,5.703717e-8,0.00002382092,0.001007254,0.00006526665,0.00002064198,0.00004718177,0.000007852119,0.00005486978],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09663459,"threshold_uncertainty_score":0.2153978,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008921249369471999,"score_gpt":0.242383239094062,"score_spread":0.23346198972459,"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."}}