{"id":"W2270363614","doi":"","title":"Статистические исследования мирового производства зерна ячменя","year":2015,"lang":"ru","type":"article","venue":"Политематический сетевой электронный научный журнал Кубанского государственного аграрного университета","topic":"Agriculture and Biological Studies","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Agriculture; Productivity; Livestock; Geography; Production (economics); Crop; Agricultural science; World market; Agricultural economics; Agronomy; Business; Biology; Economics; Forestry; Economic growth; International trade","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","open_science","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","sts","research_integrity","insufficient_payload"],"category_scores_codex":[0.004687305,0.005837532,0.005951904,0.0005259785,0.003820105,0.002343018,0.006676545,0.004586277,0.00807019],"category_scores_gemma":[0.002880062,0.003036377,0.00336126,0.006636,0.003495513,0.002483858,0.004736169,0.004795115,0.0152146],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001854311,"about_ca_system_score_gemma":0.000816064,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00372715,"about_ca_topic_score_gemma":0.005001482,"domain_scores_codex":[0.9710205,0.002559178,0.005720454,0.007080123,0.005391796,0.008227945],"domain_scores_gemma":[0.9829623,0.002143434,0.003114826,0.002363992,0.003349744,0.006065737],"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":[0.003623705,0.007505053,0.0565613,0.0006212862,0.003312855,0.002139799,0.005023312,0.0002580791,0.04201661,0.01499019,0.7017929,0.1621549],"study_design_scores_gemma":[0.006475183,0.006484117,0.1364081,0.0009921492,0.001636864,0.0007705236,0.01405251,0.0003375108,0.00520948,0.01372481,0.8048142,0.009094578],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7450492,0.04395131,0.0002612798,0.03806127,0.01559755,0.00714354,0.003122355,0.0042006,0.1426129],"genre_scores_gemma":[0.9213937,0.005218785,0.001205058,0.008905144,0.01587343,0.0007213901,0.002357712,0.0001973295,0.04412746],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1763445,"threshold_uncertainty_score":0.9992164,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09582736152808455,"score_gpt":0.2337618640012908,"score_spread":0.1379345024732062,"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."}}