{"id":"W2625154492","doi":"10.1002/2016wr019729","title":"Mixing as a driver of temporal variations in river hydrochemistry: 2. Major and trace element concentration dynamics in the Andes‐Amazon transition","year":2017,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Groundwater and Isotope Geochemistry","field":"Earth and Planetary Sciences","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Clarendon Fund; Center for Dark Energy Biosphere Investigations; National Science Foundation","keywords":"Tributary; Trace element; Hydrology (agriculture); Drainage basin; Surface runoff; STREAMS; Dilution; Environmental science; Floodplain; Foreland basin; Biogeochemical cycle; Geology; Environmental chemistry; Chemistry; Structural basin; Geomorphology; Ecology; Geochemistry; Geography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0002861464,0.0001125608,0.0002206234,0.0005072506,0.000306265,0.0007356222,0.0003479147,0.0003773236,0.001073754],"category_scores_gemma":[0.0008606661,0.0002026244,0.000218031,0.0006753648,0.0004278785,0.0004909383,0.000540561,0.0002276438,0.0001130122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007700525,"about_ca_system_score_gemma":0.0003733113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05920797,"about_ca_topic_score_gemma":0.0582497,"domain_scores_codex":[0.999891,0.0000178214,0.000008311388,0.00004012333,0.00001600391,0.00002666837],"domain_scores_gemma":[0.999674,0.00006824846,0.0001204221,0.00002202817,0.00006095464,0.00005445223],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001243144,0.00004130048,0.9831186,0.00001783973,0.000050516,0.0001003969,0.0007125861,0.0003582775,0.01226731,0.000233275,0.000116301,0.002859345],"study_design_scores_gemma":[0.000004424085,0.00001004498,0.9980167,0.000001598752,0.00000863963,0.00002160467,0.0003059919,0.001051676,0.000359326,0.00004069489,0.0001758637,0.000003421293],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.999466,0.00003297184,0.00007580894,0.00006099785,6.748974e-7,0.000004010434,0.000136383,0.000006938005,0.0002160867],"genre_scores_gemma":[0.9998325,0.00001208252,0.00003837414,0.000005432291,0.000001288165,0.000002190983,0.00006004588,0.000001975922,0.0000459628],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05920797,"threshold_uncertainty_score":0.1177267,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02062774563983617,"score_gpt":0.2736280904246637,"score_spread":0.2530003447848275,"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."}}