{"id":"W4406735471","doi":"10.48550/arxiv.2501.11396","title":"Band representations in Strongly Correlated Settings: The Kitaev Honeycomb Model","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Advanced Condensed Matter Physics","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministerio de Ciencia e Innovación; Canada Excellence Research Chairs, Government of Canada; Deutsche Forschungsgemeinschaft; European Commission","keywords":"Honeycomb; Statistical physics; Theoretical physics; Physics; Condensed matter physics; 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":[0.0001161946,0.0003659976,0.0003826342,0.00009205748,0.0001598327,0.00006921615,0.000603435,0.0001218484,0.00008150799],"category_scores_gemma":[0.00001620287,0.0003309835,0.0001920363,0.000235888,0.00009686342,0.0001430222,0.0006437477,0.001389661,0.00007237829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007615237,"about_ca_system_score_gemma":0.0002735329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000307442,"about_ca_topic_score_gemma":0.00001647196,"domain_scores_codex":[0.9982089,0.00009073666,0.0004735113,0.0006569748,0.0001847461,0.0003851488],"domain_scores_gemma":[0.9981942,0.0002038814,0.000300417,0.001129766,0.0001117982,0.00005993572],"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":[0.0000201185,0.0002096923,0.2750506,0.00005661447,0.0002405538,0.000004344727,0.001488201,0.7110769,0.0005225513,0.005928366,0.004644422,0.0007575767],"study_design_scores_gemma":[0.004901607,0.0000675136,0.1048372,0.001695753,0.0008755262,0.000001505585,0.004664679,0.4636833,0.008313004,0.4048751,0.002924511,0.003160276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9048156,0.00009307195,0.06352575,0.002290298,0.0006254448,0.0009526687,0.0004146535,0.0001175166,0.027165],"genre_scores_gemma":[0.9931428,0.000004865397,0.0004893206,0.0002099337,0.0002038331,0.0002432618,0.0003153308,0.00003778745,0.005352802],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3989467,"threshold_uncertainty_score":0.9999142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02438114990312814,"score_gpt":0.2932662035978852,"score_spread":0.2688850536947571,"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."}}