{"id":"W2891145552","doi":"10.1088/1748-9326/aae35d","title":"Watershed slope as a predictor of fluvial dissolved organic matter and nitrate concentrations across geographical space and catchment size in the Arctic","year":2018,"lang":"en","type":"article","venue":"Environmental Research Letters","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":75,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Science Foundation","keywords":"Watershed; Dissolved organic carbon; Arctic; Environmental science; Nitrate; Drainage basin; Hydrology (agriculture); Spring (device); Nutrient; Organic matter; Ecology; Oceanography; Geology; Geography; Biology","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":[0.0009050622,0.0001772386,0.0002757059,0.0004998706,0.0004377062,0.0006438369,0.0001293493,0.0001874293,0.0003945752],"category_scores_gemma":[0.001147348,0.0001511447,0.0002846639,0.0008229422,0.0003266618,0.0002396882,0.0004244077,0.0002148296,0.00007930424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004529439,"about_ca_system_score_gemma":0.0005751276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09245662,"about_ca_topic_score_gemma":0.1557093,"domain_scores_codex":[0.9997939,0.00008681481,0.0000128944,0.00004557562,0.00002497623,0.00003583527],"domain_scores_gemma":[0.9994548,0.0001755888,0.0001080435,0.00004022733,0.0001140239,0.000107331],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006577748,0.0000069228,0.9971179,0.000003830918,0.00005796081,0.00002100813,0.0001242137,0.0003373458,0.0003999423,0.00002235347,0.00003978701,0.001802872],"study_design_scores_gemma":[0.000001262896,0.00001172522,0.9990265,0.000002953588,0.00002001214,0.00001469511,0.000192723,0.0005170684,0.00006310468,0.00002619312,0.0001222151,0.000001521215],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9995434,0.00009662622,0.00009681457,0.00001333888,0.000001533442,6.060183e-7,0.00008117004,0.000002474622,0.0001639747],"genre_scores_gemma":[0.9995213,0.00009404023,0.00009821704,0.000007243365,0.000002078333,0.000001943363,0.0001897711,0.00000280857,0.00008273205],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09245662,"threshold_uncertainty_score":0.1838369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02341903594249199,"score_gpt":0.2742533823300607,"score_spread":0.2508343463875687,"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."}}