{"id":"W4388482235","doi":"10.48550/arxiv.2311.03334","title":"Emergent magnetic order in the antiferromagnetic Kitaev model with a [111] field","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Condensed Matter Physics","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canada First Research Excellence Fund","keywords":"Quantum spin liquid; Physics; Antiferromagnetism; Quantum; Homogeneous space; Phase diagram; Spin (aerodynamics); Topological order; Phase (matter); Quantum phases; Magnetic field; Toric code; Condensed matter physics; Field (mathematics); Topological quantum computer; Topology (electrical circuits); Quantum phase transition; Quantum mechanics; Spin polarization; Mathematics; Geometry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00008028436,0.0003461504,0.0002917414,0.0001234559,0.00008833307,0.00004643856,0.0007578337,0.00008989931,0.000182358],"category_scores_gemma":[0.000003556206,0.0003037645,0.0001232369,0.0005433779,0.00007673187,0.00008639292,0.0004764137,0.000711687,0.0001125279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003945165,"about_ca_system_score_gemma":0.0001425736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004553965,"about_ca_topic_score_gemma":0.0001215393,"domain_scores_codex":[0.9985304,0.00007670821,0.0001780639,0.0007222906,0.0001063187,0.0003862498],"domain_scores_gemma":[0.998672,0.0001065102,0.0001348954,0.0009416663,0.00007772286,0.00006719309],"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":[0.00005749586,0.0001588547,0.006135696,0.00005531403,0.00006677547,0.0001295905,0.0005033539,0.9539204,0.00003401988,0.03748341,0.000976413,0.0004786707],"study_design_scores_gemma":[0.001031674,0.0002235641,0.001274629,0.0001581074,0.0002407603,0.000001058915,0.001201513,0.6592357,0.0000766908,0.3355514,0.0001884286,0.0008165512],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7119963,0.00002364123,0.2780083,0.0005728884,0.0001865517,0.0006881804,0.0000541905,0.00009882708,0.008371117],"genre_scores_gemma":[0.9943904,0.00001452099,0.0005170782,0.0001877782,0.00008438696,0.00001002181,0.00004273108,0.00004660132,0.004706492],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.298068,"threshold_uncertainty_score":0.9999415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06134391101346543,"score_gpt":0.194712101924906,"score_spread":0.1333681909114406,"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."}}