{"id":"W2892100124","doi":"10.1175/jtech-d-18-0088.1","title":"A Real-Time Online Data Product that Automatically Detects Easterly Gap-Flow Events and Precipitation Type in the Columbia River Gorge","year":2018,"lang":"en","type":"article","venue":"Journal of Atmospheric and Oceanic Technology","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Office of Energy Efficiency and Renewable Energy; National Oceanic and Atmospheric Administration","keywords":"Precipitation; Environmental science; Snow; Precipitation types; Meteorology; Rain and snow mixed; Streamflow; Climatology; Rain gauge; Drainage basin; Geology; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003064931,0.0006743678,0.0004497802,0.002450981,0.000196754,0.0006800593,0.0004820732,0.0003009567,0.006664413],"category_scores_gemma":[0.001228383,0.0002422564,0.0001522416,0.0009966001,0.0001712058,0.0003745868,0.000366193,0.0002344957,0.001584595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004423437,"about_ca_system_score_gemma":0.0005233954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03278919,"about_ca_topic_score_gemma":0.0466884,"domain_scores_codex":[0.9997571,0.00002448286,0.00001618269,0.00007446494,0.0001013709,0.00002625585],"domain_scores_gemma":[0.9990263,0.0002157302,0.00007534158,0.00009828906,0.0004775202,0.0001067512],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003528721,0.0005991179,0.1408492,0.0006359563,0.000203221,0.001389919,0.0009537106,0.03063662,0.08979186,0.0006808405,0.1761253,0.5546056],"study_design_scores_gemma":[0.0004720653,0.000529191,0.3598584,0.0001451472,0.0001430292,0.0003650851,0.0004572784,0.5247113,0.05815284,0.0007369616,0.05420364,0.0002250144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6053373,0.0003187476,0.1078743,0.0004040231,0.0001964634,0.001123714,0.1208785,0.1472189,0.01664788],"genre_scores_gemma":[0.8201958,0.0001219743,0.1035717,0.0001550994,0.00005256019,0.0006166442,0.06627888,0.00188604,0.007121292],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03278919,"threshold_uncertainty_score":0.06519669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02685085856320628,"score_gpt":0.2473801122693746,"score_spread":0.2205292537061683,"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."}}