{"id":"W3041189593","doi":"","title":"하천 퇴적물 내 중금속 오염도 평가에 관한 연구 (낙동강 수계 표층 퇴적물을 대상으로)","year":2017,"lang":"ko","type":"article","venue":"한국물환경학회지","topic":"Ecology and Conservation Studies","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Sediment; Environmental chemistry; Heavy metals; Contamination; Pollution; Environmental science; Silt; Grain size; Tributary; Mineralogy; Chemistry; Metallurgy; Geology; Geography; Geomorphology; Materials science","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":["sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004449587,0.0003371794,0.0004665803,0.00001808787,0.003474871,0.0003416329,0.0009857691,0.0003981069,0.003406966],"category_scores_gemma":[0.0004010139,0.0001536807,0.0002369043,0.0001412741,0.0007181068,0.0003688166,0.0005408886,0.0003453648,0.00234505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003481566,"about_ca_system_score_gemma":0.00003673698,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008511803,"about_ca_topic_score_gemma":0.004428416,"domain_scores_codex":[0.9979104,0.0001434717,0.0004142702,0.0006273479,0.0002571117,0.0006473756],"domain_scores_gemma":[0.9985031,0.0003494944,0.0004225379,0.0003428778,0.0002109,0.0001710622],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005553675,0.0001785004,0.9430547,0.00002084336,0.0001107583,0.0000398698,0.0002731112,8.312243e-7,0.002666702,0.003739738,0.02098905,0.02887031],"study_design_scores_gemma":[0.0002824608,0.0002634522,0.9061208,0.00004502038,0.00005593174,0.000007467568,0.0006312262,0.00002529404,0.00034409,0.002164603,0.08970415,0.0003555432],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9404522,0.000598655,0.000001647668,0.0416648,0.001505145,0.0002576829,0.00006960554,0.0000867051,0.01536357],"genre_scores_gemma":[0.9669895,0.0009093776,0.00003917832,0.002490575,0.001013023,0.00004050653,0.00002532742,0.000002845484,0.02848973],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0687151,"threshold_uncertainty_score":0.9984317,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06675601078622782,"score_gpt":0.2741095792806407,"score_spread":0.2073535684944128,"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."}}