{"id":"W4404636396","doi":"10.1103/physrevb.110.l201114","title":"Scale-invariant magnetic anisotropy in <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mrow> <mml:mi>α</mml:mi> <mml:mtext>−</mml:mtext> <mml:msub> <mml:mi>RuCl</mml:mi> <mml:mn>3</mml:mn> </mml:msub> </mml:mrow> </mml:math> : A quantum Monte Carlo study","year":2024,"lang":"lv","type":"article","venue":"Physical review. B./Physical review. B","topic":"Advanced Condensed Matter Physics","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research","funders":"Austrian Science Fund; Deutsche Forschungsgemeinschaft","keywords":"Algorithm; Artificial intelligence; Computer science","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":["metaepi_narrow","scholarly_communication","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","insufficient_payload"],"category_scores_codex":[0.001925189,0.001921313,0.001464857,0.0003895169,0.001263816,0.001530423,0.003212426,0.0009762167,0.04110796],"category_scores_gemma":[0.0009684117,0.002952673,0.004360337,0.002708403,0.001632669,0.00205,0.002975462,0.003698627,0.007808652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006187528,"about_ca_system_score_gemma":0.00185339,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001308906,"about_ca_topic_score_gemma":0.000408979,"domain_scores_codex":[0.9850131,0.001258797,0.00321951,0.003512656,0.003450282,0.003545604],"domain_scores_gemma":[0.9890435,0.002342997,0.002376236,0.004206704,0.000305976,0.001724525],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004940709,0.001910396,0.00001794125,0.008271216,0.00163148,0.001311682,0.001544981,0.0004469337,0.003123719,0.819069,0.1415984,0.02058013],"study_design_scores_gemma":[0.005500292,0.00848087,0.0002329375,0.03837791,0.01399486,0.000726971,0.00234214,0.1553629,0.6935111,0.02697135,0.04610757,0.008391098],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8506001,0.03395709,0.004145915,0.003172588,0.003548158,0.0006087543,0.001246898,0.0005904903,0.1021301],"genre_scores_gemma":[0.9714561,0.01398317,0.0009723079,0.003141777,0.004421597,0.003640345,0.0009527015,0.001207061,0.000224937],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7920977,"threshold_uncertainty_score":0.9995061,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0173749095388412,"score_gpt":0.2886233698714491,"score_spread":0.2712484603326079,"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."}}