{"id":"W2897894482","doi":"10.1016/j.epsl.2018.09.022","title":"Ge and Si isotope signatures in rivers: A quantitative multi-proxy approach","year":2018,"lang":"en","type":"article","venue":"Earth and Planetary Science Letters","topic":"Geochemistry and Elemental Analysis","field":"Earth and Planetary Sciences","cited_by":41,"is_retracted":false,"has_abstract":false,"ca_institutions":"Trent University","funders":"Institut Carnot PolyNat; Consortium of Universities for the Advancement of Hydrologic Science; Geological Society of America; Agence Nationale de la Recherche; National Science Foundation","keywords":"Weathering; Silicate; Biogeochemical cycle; Dissolution; Geology; Silicate minerals; Environmental chemistry; Geochemistry; Earth science; Mineralogy; Carbon cycle; Chemistry; Ecosystem; Ecology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000854123,0.000577878,0.0005119162,0.001274339,0.0003074724,0.001132948,0.0007203986,0.0008021975,0.0005909365],"category_scores_gemma":[0.0007745871,0.0004851306,0.0006286243,0.001139827,0.0003086946,0.001214474,0.0006654368,0.0003295056,0.0001534289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004615106,"about_ca_system_score_gemma":0.0003795672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009077198,"about_ca_topic_score_gemma":0.01407458,"domain_scores_codex":[0.99975,0.00004881805,0.00001651607,0.0001240316,0.00003832277,0.00002221483],"domain_scores_gemma":[0.9997655,0.00008119821,0.00004057238,0.0000545295,0.00004544169,0.00001292748],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001327836,0.0002691487,0.4919622,0.0003759422,0.002083803,0.0003180273,0.0004467107,0.1221039,0.2932685,0.003463314,0.0006481407,0.08373248],"study_design_scores_gemma":[0.00006994216,0.0002102049,0.541858,0.00005004498,0.0007090876,0.0002587228,0.0005260827,0.4094372,0.03910897,0.004158767,0.003478595,0.0001344139],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9751986,0.0005295555,0.02023924,0.00009159752,0.00001032373,0.00002431279,0.001808747,0.000147013,0.00195048],"genre_scores_gemma":[0.9895892,0.0001332923,0.008914685,0.00002614207,0.000005845793,0.00001468446,0.0007623737,0.00002870129,0.0005250798],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009077198,"threshold_uncertainty_score":0.0180487,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01409094036348027,"score_gpt":0.209610347347162,"score_spread":0.1955194069836818,"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."}}