{"id":"W4250760723","doi":"10.5194/cp-2020-87","title":"Reconstructing past hydrology of eastern Canadian boreal catchments using clastic varved sediments and hydro-climatic modeling: 160 years of fluvial inflows","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"NanoQuébec (Canada); University of Saskatchewan; Université Laval; Center for Northern Studies; Institut National de la Recherche Scientifique","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Hydro-Québec","keywords":"Varve; Tributary; Geology; Hydrology (agriculture); Series (stratigraphy); Sediment; Fluvial; Sedimentology; Clastic rock; Period (music); Discharge; Physical geography; Geomorphology; Climatology; Drainage basin; Structural basin; Paleontology; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0002986231,0.0003564641,0.0001849727,0.001285587,0.0008550228,0.0009044644,0.0005097077,0.0002732112,0.0005868255],"category_scores_gemma":[0.0006660774,0.0002659976,0.0004661684,0.001257419,0.0003532463,0.0002475657,0.0002983058,0.0002213763,0.00009580285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005809075,"about_ca_system_score_gemma":0.003518615,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9252281,"about_ca_topic_score_gemma":0.9584398,"domain_scores_codex":[0.9999105,0.000009029909,0.000005362052,0.00003064498,0.00001630678,0.00002819408],"domain_scores_gemma":[0.9997837,0.00002856696,0.00003733076,0.00001747373,0.00007530514,0.00005762398],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001238586,0.00006586519,0.9171527,0.00004050553,0.0002166752,0.0002148322,0.0003579313,0.06327286,0.002145268,0.0003372181,0.0009408423,0.01513145],"study_design_scores_gemma":[0.00001072211,0.00001145827,0.9086897,0.00001380436,0.00003515419,0.00003627695,0.0003370805,0.08942165,0.0002379735,0.00006348341,0.001124328,0.00001846548],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9983256,0.00007199932,0.0002169007,0.00002906262,0.000001742542,0.000005404205,0.0009353395,0.00003205679,0.0003817411],"genre_scores_gemma":[0.9984067,0.00004919245,0.0003850726,0.000004924317,0.000001287472,0.000003391196,0.001000797,0.00000593888,0.0001426871],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07477194,"threshold_uncertainty_score":0.1504245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03396611013786655,"score_gpt":0.2429047890359115,"score_spread":0.2089386788980449,"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."}}