{"id":"W1996769351","doi":"10.1175/2009waf2222337.1","title":"High-Resolution GEM-LAM Application in Marine Fog Prediction: Evaluation and Diagnosis","year":2009,"lang":"en","type":"article","venue":"Weather and Forecasting","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada; Dalhousie University","funders":"Canadian Foundation for Climate and Atmospheric Sciences; Public Health Agency of Canada","keywords":"Environmental science; Meteorology; Boundary layer; Energy balance; Water vapor; High resolution; Condensation; Atmospheric sciences; Computer science; Remote sensing; Geology; Physics; Mechanics","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":[0.0004576897,0.00006730368,0.00008868818,0.00005559552,0.0001227517,0.00002829858,0.00002755813,0.00004593614,0.0002701414],"category_scores_gemma":[0.00006713367,0.00005455315,0.00001107148,0.0001221507,0.00002312885,0.0001341468,0.000004310411,0.00006057696,0.000004465147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004898062,"about_ca_system_score_gemma":0.00000472641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003986499,"about_ca_topic_score_gemma":0.0004582762,"domain_scores_codex":[0.9993709,0.00004457916,0.0001648145,0.0001794702,0.0001182987,0.0001219133],"domain_scores_gemma":[0.9997171,0.0001110405,0.00004422834,0.00005823715,0.00002264122,0.00004682285],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001139651,0.000007959489,0.567641,0.00000222317,0.000001435584,2.324673e-7,0.00007524052,0.009432718,0.00001488439,0.0002362379,0.000004436796,0.4225722],"study_design_scores_gemma":[0.0002371406,0.00008615734,0.6628945,0.000004409434,0.000007812377,0.000001781752,0.00001997526,0.3239324,0.000002534989,0.01269432,0.00007748196,0.00004151587],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9916936,0.0003276745,0.0006574365,0.0002066652,0.00003828854,0.0002476087,0.000009869222,0.00002202224,0.00679679],"genre_scores_gemma":[0.9983283,0.00004197102,0.001331113,0.00008556945,0.00009063488,0.000008586811,0.00008881566,0.000001205144,0.00002377129],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4225307,"threshold_uncertainty_score":0.2957859,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03735472230998992,"score_gpt":0.2375622449576836,"score_spread":0.2002075226476936,"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."}}