{"id":"W2583615333","doi":"10.1103/physrevb.95.205121","title":"Ising antiferromagnet in the two-dimensional Hubbard model with mismatched Fermi surfaces","year":2017,"lang":"en","type":"article","venue":"Physical review. B./Physical review. B","topic":"Cold Atom Physics and Bose-Einstein Condensates","field":"Physics and Astronomy","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"European Research Council; Ministry of Science and Technology of the People's Republic of China; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Ising model; Physics; Quantum Monte Carlo; Hubbard model; Antiferromagnetism; Monte Carlo method; Ground state; Condensed matter physics; Monte Carlo method in statistical physics; Phase diagram; Spin (aerodynamics); Statistical physics; Quantum mechanics; Hybrid Monte Carlo; Phase (matter); Mathematics; 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":[0.0003966603,0.0006919146,0.001014635,0.001114958,0.0009638476,0.001210005,0.001124776,0.001237652,0.001352221],"category_scores_gemma":[0.0008813021,0.0002705274,0.0006409718,0.0008487728,0.001804747,0.001402049,0.0006814172,0.0006962041,0.0001542498],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008262269,"about_ca_system_score_gemma":0.0008136422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006128852,"about_ca_topic_score_gemma":0.004821361,"domain_scores_codex":[0.9998707,0.00005462044,0.00000430211,0.00001121937,0.00002372137,0.0000354384],"domain_scores_gemma":[0.9996093,0.0001572727,0.00008622932,0.00003177698,0.00003927153,0.00007613558],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004192165,0.0002034288,0.002501429,0.0002585145,0.0001309064,0.001272333,0.0002575652,0.2221093,0.01336175,0.7549306,0.001607454,0.002947617],"study_design_scores_gemma":[0.0001424593,0.00008437297,0.0008786673,0.00002306629,0.00003994462,0.0001857644,0.00009206378,0.8247573,0.001321061,0.1717367,0.0007061283,0.00003236554],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9681437,0.00190121,0.01822278,0.0009200859,0.0001130733,0.00002545558,0.00008212391,0.000139304,0.0104523],"genre_scores_gemma":[0.9951487,0.0005460429,0.002465766,0.00004373839,0.00006270783,0.00001717773,0.00004688714,0.00001706129,0.001651971],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006128852,"threshold_uncertainty_score":0.01218635,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0283703069992517,"score_gpt":0.3723993511673626,"score_spread":0.3440290441681109,"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."}}