{"id":"W349913240","doi":"","title":"Генерального штаба генерал-майор М. Е. Солнышкин: от храбого Терского казака и «Мятежника-корниловца» к добросовестному служаке в провинциальных штабах Красной армии. 1914-1924 гг.","year":2014,"lang":"ru","type":"article","venue":"Новейшая история России","topic":"Social and Behavioral Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Front (military); Military service; Mutiny; Quarter (Canadian coin); Ancient history; History; Law; Political science; Economic history; Engineering; Archaeology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","sts","research_integrity","insufficient_payload"],"category_scores_codex":[0.004485559,0.00340549,0.004303351,0.0009163058,0.007550612,0.001892429,0.004675572,0.002570872,0.007039048],"category_scores_gemma":[0.002551561,0.003341942,0.002643441,0.004838028,0.005056378,0.002419373,0.002213941,0.003149941,0.01312015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001744989,"about_ca_system_score_gemma":0.001310209,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04412045,"about_ca_topic_score_gemma":0.01506865,"domain_scores_codex":[0.9768283,0.002771039,0.00371061,0.004359042,0.005625628,0.006705429],"domain_scores_gemma":[0.9885395,0.002072261,0.002131865,0.002934592,0.001573341,0.002748504],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001245272,0.007513396,0.1473022,0.001326701,0.002668091,0.0009341912,0.09231601,0.0001796111,0.003912312,0.07131577,0.3486454,0.322641],"study_design_scores_gemma":[0.004675775,0.001651212,0.02180066,0.0008521736,0.001893439,0.00006557213,0.03881979,0.0003630581,0.001061326,0.01264895,0.9101644,0.006003669],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.655371,0.01361403,0.0004436035,0.02741097,0.02653601,0.005761839,0.001123377,0.003465523,0.2662737],"genre_scores_gemma":[0.9179044,0.005172928,0.0007817479,0.003397287,0.01301631,0.0003735899,0.0002449917,0.0005541886,0.05855453],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.561519,"threshold_uncertainty_score":0.9991499,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06366284257393778,"score_gpt":0.3529881611100785,"score_spread":0.2893253185361407,"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."}}