{"id":"W1966212788","doi":"10.1175/bams-d-13-00131.1","title":"The MATERHORN: Unraveling the Intricacies of Mountain Weather","year":2015,"lang":"en","type":"article","venue":"Bulletin of the American Meteorological Society","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":193,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Mesoscale meteorology; Terrain; Meteorology; Multidisciplinary approach; Forcing (mathematics); Weather modification; Weather forecasting; Environmental science; Geography; Climatology; Geology; Cartography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.0003837927,0.0002478269,0.0002428267,0.0002943616,0.0002488671,0.001421133,0.0003239126,0.0003416945,0.001088394],"category_scores_gemma":[0.001071351,0.0001796883,0.0003045459,0.0003933097,0.0005013291,0.00158655,0.0009394796,0.0007576683,0.0001292227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002281605,"about_ca_system_score_gemma":0.0003747472,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01023147,"about_ca_topic_score_gemma":0.01008219,"domain_scores_codex":[0.9999167,0.00003329746,0.000003950656,0.00001708291,0.000018236,0.00001075449],"domain_scores_gemma":[0.9997,0.000130189,0.00005257397,0.00005673304,0.00002846556,0.00003215941],"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":[0.0002015857,0.0001259147,0.1176677,0.0003778584,0.00021218,0.0002947477,0.0006200678,0.605984,0.01441989,0.07229072,0.01545352,0.1723517],"study_design_scores_gemma":[0.00002578578,0.00004179096,0.03739978,0.00005819029,0.00002632337,0.00004075771,0.0002478859,0.9237628,0.000978508,0.02123933,0.01614996,0.00002893152],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8132985,0.007321106,0.1422438,0.008339067,0.0004776661,0.00006595002,0.001900158,0.001956463,0.02439714],"genre_scores_gemma":[0.9704875,0.002023883,0.02569381,0.0001440639,0.0002908902,0.00001652818,0.0004829482,0.000108584,0.0007517788],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01023147,"threshold_uncertainty_score":0.02034384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02599215172614684,"score_gpt":0.2327249425560056,"score_spread":0.2067327908298588,"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."}}