{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006066397,0.0003863395,0.0003719823,0.0002489644,0.0002354721,0.0005303389,0.0008806739,0.0004431357,0.001152788],"category_scores_gemma":[0.001619246,0.0001932331,0.0003461274,0.0001832285,0.0002614686,0.0003297152,0.000439167,0.000331884,0.0001444633],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000570831,"about_ca_system_score_gemma":0.0005331925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02183667,"about_ca_topic_score_gemma":0.01623667,"domain_scores_codex":[0.9998702,0.00004041856,0.000008024927,0.0000228574,0.00003475314,0.00002379229],"domain_scores_gemma":[0.9996157,0.0001399485,0.00002578187,0.00006507897,0.00009579548,0.00005771807],"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":[0.0008805931,0.0003453308,0.02702458,0.0000901214,0.00008179403,0.0004781084,0.0001426928,0.9049115,0.02238997,0.001026153,0.001476265,0.04115295],"study_design_scores_gemma":[0.00002344002,0.00002748082,0.001227704,0.000001568105,0.000003989593,0.000008891715,0.0000100086,0.996637,0.001868885,0.00004527318,0.0001423281,0.000003454731],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9576653,0.00009114466,0.035192,0.0001631702,0.00004993877,0.00008128707,0.0004782892,0.003585333,0.002693566],"genre_scores_gemma":[0.981446,0.00001948132,0.01798533,0.00001900748,0.000003620131,0.0000109033,0.0001988581,0.00004780549,0.0002689224],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02183667,"threshold_uncertainty_score":0.04341912,"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."}}