{"id":"W4386465976","doi":"10.5194/gmd-16-5069-2023","title":"Passive-tracer modelling at super-resolution with Weather Research and Forecasting – Advanced Research WRF (WRF-ARW) to assess mass-balance schemes","year":2023,"lang":"en","type":"article","venue":"Geoscientific model development","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; Environment and Climate Change Canada","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada; Environment and Climate Change Canada","keywords":"Weather Research and Forecasting Model; Downscaling; Meteorology; Environmental science; Terrain; Temporal resolution; Grid; Precipitation; Physics; Geography","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00269534,0.0002943584,0.0002425132,0.0001094233,0.001824213,0.0001672712,0.0004677673,0.0001242196,0.0001904138],"category_scores_gemma":[0.00004540403,0.0002588672,0.0000307604,0.001562325,0.0007072711,0.0003637998,0.001351003,0.0004212953,0.0006506282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001426634,"about_ca_system_score_gemma":0.00008146276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001157258,"about_ca_topic_score_gemma":0.0001477815,"domain_scores_codex":[0.9944003,0.0001126678,0.0004021375,0.001371588,0.002191763,0.001521543],"domain_scores_gemma":[0.9988049,0.0001300434,0.00006764432,0.0005221974,0.00007193311,0.0004032687],"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":[0.00009610951,0.00006659397,0.01411424,0.00002577949,0.00001084823,0.00001702633,0.001372457,0.9587552,0.01427577,0.00009933291,0.0009126669,0.01025398],"study_design_scores_gemma":[0.000353681,0.00006932778,0.00729477,0.00008188333,0.000003149331,0.000005965633,0.0006863088,0.9770599,0.001468808,0.0006463959,0.01194152,0.0003883118],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7827348,0.00002719275,0.2146886,0.000295162,0.00010119,0.0006213903,0.000007822829,0.00008206359,0.001441748],"genre_scores_gemma":[0.6740037,0.00006723795,0.2661701,0.00003770652,0.00001653568,0.0003241728,0.00003696383,0.00005865206,0.05928494],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1087311,"threshold_uncertainty_score":0.9999864,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.145016345933497,"score_gpt":0.318555771129512,"score_spread":0.173539425196015,"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."}}