{"id":"W4386977231","doi":"10.5194/gmd-16-5427-2023","title":"The Canadian Atmospheric Model version 5 (CanAM5.0.3)","year":2023,"lang":"en","type":"article","venue":"Geoscientific model development","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Environmental science; Climatology; Climate model; Radiative transfer; Atmospheric sciences; Precipitation; Atmospheric models; Atmosphere (unit); Aerosol; Meteorology; Cloud fraction; Amazonian; Water cycle; Atmospheric model; Cloud computing; Cloud cover; Climate change; Geography; Geology; Physics; Amazon rainforest; Computer science","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001276755,0.001445646,0.001208732,0.002057663,0.001585235,0.002796623,0.003555279,0.001321118,0.02657922],"category_scores_gemma":[0.002804129,0.0006727246,0.001765575,0.007592004,0.0003944371,0.001751282,0.0009652376,0.002521578,0.01003136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00774248,"about_ca_system_score_gemma":0.02030235,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9317854,"about_ca_topic_score_gemma":0.898838,"domain_scores_codex":[0.9991013,0.0001109781,0.00004322662,0.0001190374,0.0004858489,0.0001397042],"domain_scores_gemma":[0.9980071,0.0000741387,0.00007320797,0.0001309061,0.00156469,0.000149985],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001743816,0.00005148906,0.006588696,0.0007585546,0.0004619142,0.0001233894,0.0001160058,0.03195744,0.001273461,0.01079304,0.9101272,0.03757446],"study_design_scores_gemma":[0.0003091831,0.00002007719,0.01531094,0.0002481115,0.0002211707,0.00004828207,0.0001210979,0.04648494,0.001032936,0.004709328,0.9313362,0.0001576459],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.005733383,0.004120653,0.02644969,0.002520033,0.0009847259,0.0005170993,0.8827882,0.01345427,0.06343199],"genre_scores_gemma":[0.08228856,0.005548813,0.07815389,0.0013124,0.0003077287,0.001175286,0.7988729,0.00434691,0.02799347],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0682146,"threshold_uncertainty_score":0.1372326,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01083367367887888,"score_gpt":0.189862905862778,"score_spread":0.1790292321838991,"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."}}