{"id":"W3102608271","doi":"10.1002/hyp.13982","title":"Linking hydrological variations at local scales to regional climate teleconnection patterns","year":2020,"lang":"en","type":"article","venue":"Hydrological Processes","topic":"Climate variability and models","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Environment and Climate Change Canada","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Teleconnection; Surface runoff; Environmental science; Precipitation; North Atlantic oscillation; Climatology; Snow; Structural basin; Water cycle; Climate change; Atmospheric sciences; Hydrology (agriculture); Geology; Geography; Ecology; Meteorology; Oceanography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002351444,0.0002217212,0.0002608219,0.00002313747,0.0004094875,0.00004732306,0.0003257071,0.0002232297,0.00337482],"category_scores_gemma":[0.0003818573,0.0001742039,0.00007868401,0.0004163671,0.0002288955,0.0002055307,0.0006339105,0.0002374426,0.00144149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001350588,"about_ca_system_score_gemma":0.000009666616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003395592,"about_ca_topic_score_gemma":0.0002361524,"domain_scores_codex":[0.997897,0.00009571233,0.0003546939,0.0007938684,0.0003372708,0.0005214756],"domain_scores_gemma":[0.9990647,0.0003009495,0.00008724359,0.0001735278,0.00002107957,0.0003524988],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001370916,0.00108345,0.2778696,0.0003899109,0.00003994493,0.00008097683,0.001951186,0.6891651,0.01417704,0.00195363,0.001953687,0.009964592],"study_design_scores_gemma":[0.001927061,0.004057146,0.170259,0.000101105,0.000143906,0.0002041665,0.0001820475,0.7272741,0.003350812,0.03272656,0.05751885,0.002255241],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9332218,0.00002259783,0.05178837,0.01215453,0.00004547586,0.0003027488,0.00002885486,0.0002750009,0.002160607],"genre_scores_gemma":[0.9876499,0.00007144015,0.0008758572,0.01111377,0.0001281541,0.00008068959,0.00004908521,0.00001352598,0.00001756286],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1076107,"threshold_uncertainty_score":0.999336,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04253026800930903,"score_gpt":0.250656069284492,"score_spread":0.2081258012751829,"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."}}