{"id":"W2768734947","doi":"10.1007/s10661-017-6353-0","title":"Trace-metal contamination in the glacierized Rio Santa watershed, Peru","year":2017,"lang":"en","type":"article","venue":"Environmental Monitoring and Assessment","topic":"Heavy metals in environment","field":"Environmental Science","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; École de Technologie Supérieure; Université du Québec à Montréal","funders":"National Science Foundation","keywords":"Ecotoxicology; Environmental science; Watershed; Contamination; Trace metal; TRACE (psycholinguistics); Water resource management; Hydrology (agriculture); Environmental chemistry; Geology; Ecology; Metal; Chemistry; Biology; Computer science","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":[],"consensus_categories":[],"category_scores_codex":[0.0009858361,0.0002733005,0.0002296811,0.00003039093,0.0007903022,0.0002024022,0.000569611,0.00008676581,0.0004042983],"category_scores_gemma":[0.00001789522,0.0002065692,0.00007062812,0.00002966957,0.0005197215,0.0005391712,0.0004520681,0.0003625015,0.0001428348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005234493,"about_ca_system_score_gemma":0.000004808141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002159784,"about_ca_topic_score_gemma":0.00001205979,"domain_scores_codex":[0.9978319,0.0002017117,0.0003172542,0.0005238451,0.0006962815,0.0004289718],"domain_scores_gemma":[0.9989298,0.00007031527,0.0001638597,0.0007240772,4.942606e-7,0.0001114339],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001432663,0.0003018091,0.8928611,0.000005402819,0.00001467272,0.00004267588,0.001103309,0.00008644046,0.05770211,0.00001628572,0.0000361738,0.04781569],"study_design_scores_gemma":[0.0008704762,0.0001061087,0.9785203,0.00001675622,0.00002598706,0.00001962222,0.001528773,0.0002418951,0.005587688,0.0000749353,0.01275619,0.0002512227],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9928837,0.000119651,0.00007841525,0.00048916,0.0003543842,0.0004349597,0.000007237883,0.00001616616,0.00561635],"genre_scores_gemma":[0.9968814,0.0005062054,0.001661496,0.00003761704,0.0001399016,0.0001238528,0.000007909975,0.00002305706,0.0006185081],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08565925,"threshold_uncertainty_score":0.8423646,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01975753441339859,"score_gpt":0.2926715543687675,"score_spread":0.2729140199553689,"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."}}