{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003135044,0.0007493054,0.0005292332,0.001821858,0.003146548,0.01334266,0.001444516,0.002691253,0.0885598],"category_scores_gemma":[0.008454025,0.001016282,0.0007927015,0.002549536,0.004551257,0.006982514,0.003259652,0.003163512,0.0383031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003740133,"about_ca_system_score_gemma":0.006146988,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00841435,"about_ca_topic_score_gemma":0.01006737,"domain_scores_codex":[0.9961123,0.0009188502,0.000221703,0.0006966323,0.001742963,0.0003076034],"domain_scores_gemma":[0.9963441,0.001043124,0.0002372276,0.0006799736,0.001318403,0.000377213],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000128075,0.00008317145,0.001355196,0.0004666724,0.00002977216,0.0003254254,0.002272256,0.001164521,0.00288543,0.669287,0.1201074,0.2018952],"study_design_scores_gemma":[0.00002065329,0.00002590417,0.00103955,0.0002207797,0.00002119884,0.0002559321,0.001029657,0.0009257365,0.00147726,0.08586614,0.9090772,0.0000400033],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.008561859,0.01117029,0.08432098,0.02431271,0.003537479,0.0002766203,0.001216449,0.001309784,0.8652939],"genre_scores_gemma":[0.2160241,0.02085759,0.1289067,0.003414843,0.002216056,0.0009916531,0.001476076,0.001697138,0.6244159],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0885598,"threshold_uncertainty_score":0.2962621,"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."}}