{"id":"W2082921542","doi":"10.1080/07055900.2013.798778","title":"Dynamical Downscaling over the Gulf of St. Lawrence using the Canadian Regional Climate Model","year":2013,"lang":"en","type":"article","venue":"ATMOSPHERE-OCEAN","topic":"Climate variability and models","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bedford Institute of Oceanography; Fisheries and Oceans Canada","funders":"Canadian Meteorological and Oceanographic Society; U.S. Department of Energy","keywords":"Downscaling; Climatology; Environmental science; Climate model; Sea surface temperature; Climate change; Precipitation; Meteorology; Geology; Geography; Oceanography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.0003815488,0.0003225044,0.00017799,0.0006682443,0.0009408174,0.0008487154,0.0008683293,0.0002276553,0.001719948],"category_scores_gemma":[0.001298861,0.0001617312,0.0004011096,0.001441489,0.0002757588,0.0004489791,0.0003915197,0.0004487541,0.0002277246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01318831,"about_ca_system_score_gemma":0.01672134,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9893247,"about_ca_topic_score_gemma":0.9912822,"domain_scores_codex":[0.9998434,0.0000155618,0.00000632446,0.00004054893,0.00006499499,0.00002917787],"domain_scores_gemma":[0.9995908,0.00002725953,0.00002614919,0.00002334967,0.0003080707,0.00002443899],"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":[0.0001130077,0.0000440531,0.08635624,0.0001039405,0.0001658583,0.0001167999,0.0002655572,0.7973413,0.002708133,0.008059192,0.02016408,0.08456182],"study_design_scores_gemma":[0.00008089678,0.00002161182,0.09050816,0.00006536337,0.0001124528,0.00002592953,0.000270061,0.8641736,0.002407935,0.002327248,0.03991291,0.00009389213],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8693972,0.001840009,0.03702421,0.002549521,0.0002486399,0.0001761056,0.03206019,0.002100197,0.05460389],"genre_scores_gemma":[0.9621308,0.0008899162,0.02400029,0.000161485,0.00001605931,0.00004542764,0.007882728,0.0001530496,0.004720258],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01318831,"threshold_uncertainty_score":0.09568834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02308915888448237,"score_gpt":0.2382775314337916,"score_spread":0.2151883725493093,"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."}}