{"id":"W1637748895","doi":"10.1029/2012gl052815","title":"The impact of model fidelity on seasonal predictive skill","year":2012,"lang":"en","type":"article","venue":"Geophysical Research Letters","topic":"Climate variability and models","field":"Environmental Science","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Climatology; Environmental science; Forecast skill; Cyclostationary process; Fidelity; Forcing (mathematics); GCM transcription factors; Meteorology; Climate model; Statistics; Climate change; Computer science; General Circulation Model; Mathematics","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":[0.005645352,0.0005265913,0.0005973252,0.0003448522,0.000418422,0.001157995,0.0007482867,0.0007696244,0.0007321478],"category_scores_gemma":[0.03031944,0.000343238,0.0006600429,0.0003870396,0.0007163403,0.001695985,0.001025534,0.001168959,0.00009017302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006506882,"about_ca_system_score_gemma":0.0006939142,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01012144,"about_ca_topic_score_gemma":0.007190553,"domain_scores_codex":[0.9984543,0.0006362732,0.000153962,0.000263176,0.0003129854,0.000179102],"domain_scores_gemma":[0.9797902,0.01433558,0.001274633,0.003366619,0.0009297878,0.0003031483],"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":[0.0006296049,0.0001522167,0.06906387,0.0001201496,0.0003093549,0.0001778424,0.0001326796,0.8963469,0.01236845,0.002087738,0.0003663051,0.01824495],"study_design_scores_gemma":[0.00003680665,0.0003527144,0.0292942,0.00002501395,0.00006938024,0.00007458477,0.00006633549,0.9577598,0.01050621,0.001354996,0.0004249297,0.00003508473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9698093,0.0002581239,0.02676889,0.000353364,0.00004985022,0.00003898583,0.0003956134,0.000203474,0.002122465],"genre_scores_gemma":[0.9976828,0.00004380901,0.001992731,0.00002096712,0.000006501719,0.000009510675,0.0001288622,0.00002467418,0.00008999122],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01012144,"threshold_uncertainty_score":0.02985585,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05348547046849572,"score_gpt":0.360703001193278,"score_spread":0.3072175307247823,"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."}}