{"id":"W3157734169","doi":"","title":"낙동·고령 중권역의 표층 퇴적물 입도 조성 및 유기물질 분포 특성 변화","year":2018,"lang":"ko","type":"article","venue":"한국환경과학회지","topic":"Agriculture, Soil, Plant Science","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Loss on ignition; Total organic carbon; Sediment; Organic matter; Environmental science; Tributary; Environmental chemistry; Water quality; Nitrogen; Pollution; Watershed; Surface water; Hydrology (agriculture); Chemistry; Environmental engineering; Geology; Geography","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":[0.0001552298,0.0001769063,0.000225586,0.0007072894,0.0005198697,0.0005890965,0.0001794499,0.0001390855,0.001058763],"category_scores_gemma":[0.0002591115,0.0001224092,0.0001965028,0.001107063,0.00032155,0.0002366355,0.0002581051,0.0001424046,0.0001801288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009059448,"about_ca_system_score_gemma":0.0007494918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06244277,"about_ca_topic_score_gemma":0.1914673,"domain_scores_codex":[0.999806,0.00001078678,0.00001482743,0.00006692296,0.00006164549,0.00003990782],"domain_scores_gemma":[0.9997904,0.00001724726,0.00007084464,0.00001048541,0.00008474321,0.00002643234],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001185663,0.00003494004,0.9491061,0.00007585905,0.00005024815,0.000242796,0.0008625167,0.0002308084,0.03400196,0.00006938935,0.0001553481,0.01505146],"study_design_scores_gemma":[9.973662e-7,0.0000218641,0.9981714,0.000001678008,0.000006951032,0.0000310284,0.0002233156,0.0001032595,0.0009972235,0.00001529458,0.0004245318,0.000002367723],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9985587,0.00006613843,0.0002415536,0.00001056649,0.000001656855,0.00001343743,0.0003753866,0.000006354907,0.0007260792],"genre_scores_gemma":[0.9975189,0.00006222615,0.0006508889,0.0000148676,0.000001493891,0.00001980403,0.00056574,0.000004238789,0.001161839],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06244277,"threshold_uncertainty_score":0.1241586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02002786958561972,"score_gpt":0.2192290147981993,"score_spread":0.1992011452125795,"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."}}