{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001943647,0.0001494719,0.0003219562,0.000008251014,0.0003971599,0.00003318318,0.0009778636,0.00005672402,0.0007942504],"category_scores_gemma":[0.0007818291,0.00005277105,0.0003133045,0.0003717854,0.002170502,0.0000132558,0.0001207343,0.0002485695,0.00005389137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007703431,"about_ca_system_score_gemma":0.00002900822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001251597,"about_ca_topic_score_gemma":0.00002629633,"domain_scores_codex":[0.9979534,0.0006700213,0.0003991809,0.0002002453,0.000447353,0.0003298226],"domain_scores_gemma":[0.9969811,0.001960405,0.0004318019,0.0004311529,0.0001039138,0.00009161059],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001251867,0.000275085,0.6979831,0.00003336407,0.0006833929,0.000003125145,0.003259377,0.08981684,0.001084529,0.00812041,0.08660093,0.1108879],"study_design_scores_gemma":[0.0005335452,0.001163983,0.678403,0.000008824612,0.0001174922,0.000007646193,0.006231935,0.00783167,0.0002879026,0.02563748,0.2794725,0.0003039778],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9764881,0.001041173,0.0001978447,0.01699753,0.0001360848,0.0002611579,0.00003002887,0.00002593461,0.004822131],"genre_scores_gemma":[0.9951197,0.0001297326,0.001913241,0.002345602,0.00007113684,0.000003739749,0.000002884061,0.000003359429,0.0004106079],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1928716,"threshold_uncertainty_score":0.8696485,"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."}}