{"id":"W2946623251","doi":"10.4136/ambi-agua.2329","title":"Heavy metals in the São Mateus Stream Basin, Peixe River Basin, Paraiba do Sul River Basin, Brazil","year":2019,"lang":"en","type":"article","venue":"Ambiente e Agua - An Interdisciplinary Journal of Applied Science","topic":"Water Quality and Pollution Assessment","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Impact","funders":"Universidade Federal de Juiz de Fora","keywords":"Tributary; STREAMS; Structural basin; Environmental science; Drainage basin; Water resource management; Heavy metals; Hydrology (agriculture); Water quality; Environmental protection; Geography; Geology; Ecology; Environmental chemistry; Chemistry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.005427187,0.0004331444,0.0006105537,0.0003738213,0.0007594001,0.0003792327,0.002633164,0.0001165239,0.001675145],"category_scores_gemma":[0.0000272105,0.0002972601,0.0002714229,0.001397082,0.002308483,0.002225578,0.001417855,0.0007388273,0.000887787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000532483,"about_ca_system_score_gemma":0.0001433533,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002327173,"about_ca_topic_score_gemma":0.00009592436,"domain_scores_codex":[0.9946429,0.0003730109,0.001176456,0.0008281118,0.002055468,0.0009240815],"domain_scores_gemma":[0.9976332,0.0001707343,0.000759154,0.000953268,0.00008259017,0.0004010837],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.006619985,0.01179592,0.2223511,0.0002433606,0.0004084031,0.001463516,0.4147527,0.0284119,0.1986879,0.01713944,0.057662,0.04046389],"study_design_scores_gemma":[0.002854584,0.001982718,0.923672,0.0002586304,0.0001117173,0.0007477246,0.0239077,0.001336169,0.01922044,0.01595966,0.008850064,0.001098663],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9813133,0.00003243506,0.0004011587,0.001945363,0.0007552318,0.0005246438,0.00003002869,0.0000225874,0.01497522],"genre_scores_gemma":[0.9958256,0.00002530213,0.001602631,0.001984269,0.0001046589,0.00001327995,0.000005786912,0.00002375367,0.0004146464],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7013208,"threshold_uncertainty_score":0.999948,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01447343603354914,"score_gpt":0.3053838404605821,"score_spread":0.290910404427033,"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."}}