{"id":"W3035691046","doi":"10.33099/2707-1383-2020-35-1-5-19","title":"ІСТОРИКО-РЕТРОСПЕКТИВНИЙ АНАЛІЗ СТАНОВЛЕННЯ СИСТЕМИ ПІДГОТОВКИ МОЛОДШИХ ВІЙСЬКОВО-МЕДИЧНИХ ФАХІВЦІВ ДЛЯ ЗБРОЙНИХ СИЛ УКРАЇНИ (2015-2019 рр.)","year":2020,"lang":"uk","type":"article","venue":"Воєнно-історичний вісник","topic":"Medical and Biological Sciences","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001359073,0.0002286237,0.0001636698,0.001181011,0.002270817,0.005715061,0.0003734264,0.0008368567,0.01845447],"category_scores_gemma":[0.002012466,0.0002785656,0.0002492157,0.001486977,0.002195044,0.002000845,0.001662876,0.001319661,0.00658686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002700276,"about_ca_system_score_gemma":0.005798548,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008833509,"about_ca_topic_score_gemma":0.01377782,"domain_scores_codex":[0.9987117,0.0002644527,0.00009167577,0.0001959573,0.0005475111,0.0001886963],"domain_scores_gemma":[0.9990984,0.0001970001,0.0001048677,0.0001074615,0.0003669346,0.0001253296],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.00006772661,0.00004521791,0.003102605,0.0003874783,0.00001497536,0.0008192549,0.01451414,0.0003981287,0.002408177,0.6827526,0.03096771,0.2645219],"study_design_scores_gemma":[0.000006936637,0.00002648405,0.003788948,0.0002097595,0.00001092592,0.0005260892,0.003125718,0.0001656215,0.001461549,0.0247671,0.9658866,0.00002424384],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.06422666,0.02156409,0.02661023,0.01264677,0.002020894,0.0002361023,0.0007812676,0.0002989425,0.8716149],"genre_scores_gemma":[0.7306924,0.01529725,0.02900892,0.001258993,0.0005210786,0.0002714318,0.0005645902,0.0002626495,0.2221228],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01845447,"threshold_uncertainty_score":0.06173635,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06638493986638996,"score_gpt":0.3062990874995895,"score_spread":0.2399141476331995,"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."}}