{"id":"W4200198437","doi":"10.24852/2411-7374.2021.3.30.35","title":"АВТОМАТИЗАЦИЯ ОБРАБОТКИ ПЕРВИЧНЫХ ДАННЫХ МОНИТОРИНГА КАЧЕСТВА ВОД И ДОННЫХ ОТЛОЖЕНИЙ ПОВЕРХНОСТНЫХ ВОДНЫХ ОБЪЕКТОВ","year":2021,"lang":"ru","type":"article","venue":"Российский журнал прикладной экологии","topic":"Geological Studies and Exploration","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Water quality; Environmental science; Sanitation; Index (typography); Water resource management; Computer science; Environmental engineering; World Wide Web; Ecology","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","research_integrity","insufficient_payload"],"category_scores_codex":[0.001933193,0.002216564,0.002620093,0.0004571469,0.002886222,0.001423384,0.001941592,0.001649809,0.05229436],"category_scores_gemma":[0.0014684,0.002011931,0.001368391,0.003232451,0.001149928,0.001606743,0.0008972763,0.002349488,0.02543835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000200683,"about_ca_system_score_gemma":0.001016027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004336993,"about_ca_topic_score_gemma":0.01142962,"domain_scores_codex":[0.9854286,0.001241404,0.002805936,0.003688465,0.002626863,0.004208726],"domain_scores_gemma":[0.9922475,0.001183921,0.001091485,0.002426638,0.001098935,0.001951493],"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":[0.001792499,0.003500994,0.2729373,0.001743637,0.002982391,0.006993273,0.009150794,0.01172356,0.003307776,0.009706553,0.3817331,0.2944281],"study_design_scores_gemma":[0.00463817,0.002343642,0.2325576,0.0006887506,0.0009831476,0.0006118929,0.008647333,0.01020919,0.002957845,0.009706539,0.7216808,0.004975132],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6186898,0.0972853,0.001746746,0.03512735,0.0217319,0.00346636,0.002558047,0.001709904,0.2176846],"genre_scores_gemma":[0.9409087,0.01313864,0.002073763,0.006024621,0.004247685,0.00006929938,0.002704644,0.0001340533,0.03069861],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3399476,"threshold_uncertainty_score":0.9999521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03603575865054588,"score_gpt":0.201955791294059,"score_spread":0.1659200326435131,"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."}}