{"id":"W1988202809","doi":"10.1029/2008eo240001","title":"Development of a Pan‐Arctic Database for River Chemistry","year":2008,"lang":"en","type":"article","venue":"Eos","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":87,"is_retracted":false,"has_abstract":true,"ca_institutions":"Aboriginal Affairs Northern Dev Canada; University of Victoria","funders":"","keywords":"Arctic; Surface runoff; Environmental science; Watershed; The arctic; Oceanography; Hydrology (agriculture); Geology; Ecology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002917357,0.0007858389,0.001112871,0.006083597,0.0009461859,0.001816108,0.001679294,0.0006358472,0.009011026],"category_scores_gemma":[0.004351994,0.0005033027,0.0008976639,0.006457335,0.0001216909,0.001498242,0.00163146,0.001166055,0.00952698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001078969,"about_ca_system_score_gemma":0.004331597,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05679672,"about_ca_topic_score_gemma":0.04725505,"domain_scores_codex":[0.9984186,0.0001607521,0.0003602616,0.0004538105,0.0004867706,0.0001198053],"domain_scores_gemma":[0.9966633,0.0002896028,0.0002732051,0.0006412858,0.00178553,0.0003471928],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009739376,0.000598104,0.123917,0.001863943,0.0006493478,0.000530077,0.0008954246,0.01830287,0.01779899,0.01357236,0.5098443,0.3110536],"study_design_scores_gemma":[0.0001762148,0.00009857352,0.09923983,0.0003224979,0.0002159103,0.000263773,0.000345319,0.01847961,0.0106707,0.003386875,0.866617,0.000183621],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01569703,0.0004340795,0.02711242,0.0001864408,0.0001377176,0.0004500476,0.9399777,0.005432886,0.01057167],"genre_scores_gemma":[0.01247118,0.0002780832,0.04426844,0.00007876462,0.00003418077,0.0004826027,0.9399853,0.0003506361,0.002050753],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.05679672,"threshold_uncertainty_score":0.1129323,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07716732565910633,"score_gpt":0.2492880595398114,"score_spread":0.172120733880705,"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."}}