{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0007659832,0.0006690742,0.0006061345,0.00004318012,0.001340291,0.000514022,0.001914463,0.0005898581,0.001490099],"category_scores_gemma":[0.0002869453,0.0002551665,0.0003344241,0.001654997,0.001124223,0.0006495861,0.0005798653,0.0005470943,0.01064394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001194272,"about_ca_system_score_gemma":0.00005955588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001512063,"about_ca_topic_score_gemma":0.00285141,"domain_scores_codex":[0.9949229,0.0002543567,0.000690188,0.001445838,0.001118548,0.001568133],"domain_scores_gemma":[0.9978441,0.0003640642,0.000412112,0.0003468744,0.0003615859,0.0006712647],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0002429809,0.001327726,0.2322092,0.0001022847,0.0001818119,0.0002850452,0.002648899,0.00001467974,0.09589535,0.01015369,0.4990529,0.1578854],"study_design_scores_gemma":[0.0004488782,0.001667982,0.5896832,0.000238057,0.00009690556,0.0001903581,0.002435799,0.0003006103,0.01140464,0.001867508,0.3900896,0.001576456],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9588135,0.0007344371,0.00001488839,0.007068883,0.002750599,0.0005548067,0.0002811067,0.0002905002,0.02949126],"genre_scores_gemma":[0.9681728,0.0003353473,0.0002474867,0.002258239,0.00494122,0.00002182926,0.0001193193,0.00000541361,0.02389836],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3574741,"threshold_uncertainty_score":0.99999,"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."}}