{"id":"W33762244","doi":"10.1136/bmjopen-2020-045504","title":"РЕСУРСЫ ВЛАГИ И УРОЖАЙНОСТЬ ПРОСА НА ЧЕРНОЗёМЕ ОБЫКНОВЕННОМ В СТЕПИ ОРЕНБУРГСКОГО ПРЕДУРАЛЬЯ","year":2009,"lang":"ru","type":"article","venue":"Известия Оренбургского государственного аграрного университета","topic":"Magnetic and Electromagnetic Effects","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; Canadian Pain Society","keywords":"Moisture; Agricultural engineering; Water consumption; Environmental science; Agriculture; Water content; Water resource management; Agricultural science; Agricultural economics; Geography; Engineering; Economics; Meteorology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007209799,0.0001708684,0.0002605114,0.0007603187,0.0005594637,0.001206874,0.0002830688,0.0005324911,0.009635317],"category_scores_gemma":[0.001600527,0.0002504646,0.0002203417,0.0008357406,0.0007012321,0.0004594375,0.0002761566,0.000909149,0.002290501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006234911,"about_ca_system_score_gemma":0.001250543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00249738,"about_ca_topic_score_gemma":0.004842192,"domain_scores_codex":[0.9994228,0.0001161535,0.00003668195,0.00008703412,0.0002593603,0.00007799434],"domain_scores_gemma":[0.9994844,0.0001763583,0.0000902314,0.00006151234,0.0001205866,0.00006690896],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001843704,0.0003789017,0.008506938,0.0006893107,0.00008768669,0.0008785229,0.0009625159,0.001010877,0.6574926,0.03893429,0.007513069,0.2817016],"study_design_scores_gemma":[0.0007055721,0.002436746,0.03814628,0.0002721401,0.000383418,0.003167284,0.001308804,0.001708639,0.5128721,0.02497004,0.4138671,0.0001619387],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.636165,0.03144081,0.06139499,0.006472081,0.001413713,0.000876149,0.006099541,0.000725514,0.2554122],"genre_scores_gemma":[0.9320603,0.006115987,0.0365932,0.0003031859,0.0002054521,0.0004511279,0.00126823,0.0000892064,0.02291325],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009635317,"threshold_uncertainty_score":0.03223342,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002852760737537231,"score_gpt":0.2211150016695698,"score_spread":0.2182622409320326,"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."}}