{"id":"W3111178367","doi":"10.1103/physrevb.103.155147","title":"Modeling multiorbital effects in <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"><mml:mrow><mml:msub><mml:mi>Sr</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:msub><mml:mi>IrO</mml:mi><mml:mn>4</mml:mn></mml:msub></mml:mrow></mml:math> under strain and a Zeeman field","year":2021,"lang":"lv","type":"article","venue":"Physical review. B./Physical review. B","topic":"Advanced Condensed Matter Physics","field":"Physics and Astronomy","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; McGill University; Regroupement Québécois sur les Matériaux de Pointe","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Antiferromagnetism; Zeeman effect; Condensed matter physics; Order (exchange); Physics; Materials science; Algorithm; Thermodynamics; Magnetic field; Computer science; Quantum mechanics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003515943,0.000274333,0.0005525103,0.000305005,0.0004866161,0.0008314315,0.001196688,0.000999714,0.002135008],"category_scores_gemma":[0.000499706,0.000273089,0.0005339796,0.0003314735,0.000607686,0.0007675129,0.0004846749,0.0005882975,0.0001592083],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009241197,"about_ca_system_score_gemma":0.000950423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009436045,"about_ca_topic_score_gemma":0.0130787,"domain_scores_codex":[0.9999135,0.00002219466,0.000002645102,0.000009688881,0.00002216221,0.00002984314],"domain_scores_gemma":[0.9997841,0.00009814791,0.00004132546,0.00002756944,0.00002089929,0.00002799676],"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":[0.0001214644,0.0001649056,0.001342087,0.00009967654,0.00003514302,0.0001556635,0.0001082493,0.9087988,0.01338081,0.0724901,0.0006796497,0.002623474],"study_design_scores_gemma":[0.00001023469,0.00001886297,0.0001593541,0.000002828467,0.000004472287,0.000006949761,0.00001437258,0.9953496,0.0008847605,0.003392884,0.0001498702,0.000005870716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9561213,0.0002478382,0.02929721,0.0005519403,0.00003620226,0.00002964231,0.0001925312,0.0001373056,0.01338583],"genre_scores_gemma":[0.9909327,0.0001468216,0.005741844,0.00004541408,0.00001284452,0.00003720678,0.00009445819,0.00005645797,0.002932315],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009436045,"threshold_uncertainty_score":0.01876223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01823831474134871,"score_gpt":0.2847641216456382,"score_spread":0.2665258069042895,"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."}}