{"id":"W2794206552","doi":"","title":"Improving Consistency, Accuracy and Stability of the Global Environmental Multiscale Atmospheric Model","year":2017,"lang":"en","type":"article","venue":"97th American Meteorological Society Annual Meeting","topic":"Ecosystem dynamics and resilience","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Stability (learning theory); Consistency (knowledge bases); Environmental science; Econometrics; Climatology; Meteorology; Computer science; Mathematics; Geography; Geology; Artificial intelligence; Machine learning","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":[0.005396092,0.0006743874,0.0008440377,0.0007871129,0.0007967129,0.001651808,0.00167661,0.00150993,0.001028282],"category_scores_gemma":[0.03504347,0.0006766978,0.0009156085,0.0005408326,0.0006944249,0.002077116,0.002281968,0.002237659,0.0003207915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007151554,"about_ca_system_score_gemma":0.001892221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02095478,"about_ca_topic_score_gemma":0.01457022,"domain_scores_codex":[0.9985163,0.0006666255,0.0001317631,0.0002979159,0.0002827172,0.0001047015],"domain_scores_gemma":[0.989886,0.004931791,0.0005450128,0.002424542,0.001940169,0.0002724],"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":[0.0003862547,0.0001720691,0.02459736,0.00007322684,0.0003277405,0.0001002196,0.000147079,0.9227348,0.005442095,0.008489462,0.002399678,0.03513009],"study_design_scores_gemma":[0.00002779102,0.0000130333,0.0007850287,0.000003816904,0.00001420476,0.000004560344,0.00000820383,0.997251,0.0006458808,0.001071627,0.0001688662,0.000006020154],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.695534,0.0006399329,0.2903727,0.002205586,0.0008108055,0.0001260711,0.001217053,0.002734228,0.006359617],"genre_scores_gemma":[0.9553295,0.00006540078,0.0429588,0.0001505216,0.00009119582,0.00003503901,0.0006147912,0.0003748623,0.0003799156],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.02095478,"threshold_uncertainty_score":0.04166561,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007750829881169261,"score_gpt":0.2271136046816963,"score_spread":0.219362774800527,"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."}}