{"id":"W2764200753","doi":"10.1002/2017wr021585","title":"Nine Hundred Years of Weekly Streamflows: Stochastic Downscaling of Ensemble Tree‐Ring Reconstructions","year":2017,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Tree-ring climate responses","field":"Earth and Planetary Sciences","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"Alberta Innovates","keywords":"Streamflow; Dendrochronology; Climatology; Proxy (statistics); Downscaling; Range (aeronautics); Historical record; Series (stratigraphy); Environmental science; Data assimilation; Climate change; Geology; Drainage basin; Meteorology; Geography; Statistics; Mathematics; Oceanography; Cartography; History","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001298759,0.0001169485,0.00031265,0.0004684113,0.0005034031,0.0001632424,0.000791339,0.00009510433,0.0008473251],"category_scores_gemma":[0.000488896,0.0001011094,0.00009770975,0.0001504835,0.000773665,0.0002165453,0.0001393069,0.0003139096,0.0001654283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009864934,"about_ca_system_score_gemma":0.00004114428,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009497254,"about_ca_topic_score_gemma":0.004374583,"domain_scores_codex":[0.9976482,0.000258258,0.0003944822,0.0003322823,0.0007316876,0.0006351387],"domain_scores_gemma":[0.998263,0.0005351881,0.0001341399,0.0007569722,0.0001500746,0.000160627],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001129467,0.0001022571,0.2748894,0.0002940905,0.0001840736,0.00007536434,0.005752056,0.01255648,0.06473261,0.00003391279,0.00008826509,0.640162],"study_design_scores_gemma":[0.000967355,0.0005098901,0.9489663,0.0003816925,0.00003001922,0.00005426348,0.001668525,0.006437214,0.03821279,0.0004636795,0.002017936,0.0002903103],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9920657,0.0001864189,0.00002769541,0.0001645893,0.0001102487,0.0001900076,0.00009012169,0.00002790608,0.007137288],"genre_scores_gemma":[0.9980277,0.00003369313,0.0006202413,0.000001582127,0.0000934515,0.000002425425,0.00002136809,0.00001017586,0.00118939],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6740769,"threshold_uncertainty_score":0.9970986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06527217510855227,"score_gpt":0.3129209446927878,"score_spread":0.2476487695842355,"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."}}