{"id":"W2101490022","doi":"10.5194/npg-15-793-2008","title":"Enhancing predictability by increasing nonlinearity in ENSO and Lorenz systems","year":2008,"lang":"en","type":"article","venue":"Nonlinear processes in geophysics","topic":"Climate variability and models","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Predictability; Nonlinear system; El Niño Southern Oscillation; Oscillation (cell signaling); Lorenz system; Climatology; Control theory (sociology); Environmental science; Mathematics; Statistical physics; Meteorology; Physics; Computer science; Geology; Statistics; Control (management)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009603163,0.0003833747,0.0002842769,0.0003827212,0.0004252645,0.0007168176,0.000200732,0.0002747898,0.001184401],"category_scores_gemma":[0.007096462,0.0001887152,0.0002513602,0.000215759,0.0006228241,0.001545533,0.001552588,0.0006179399,0.0001419829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005532298,"about_ca_system_score_gemma":0.0002793499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00102113,"about_ca_topic_score_gemma":0.001362883,"domain_scores_codex":[0.9995317,0.0002365191,0.00003080041,0.00004126702,0.00009909211,0.00006064362],"domain_scores_gemma":[0.9964002,0.002434954,0.0004534679,0.000271426,0.0002956059,0.0001441942],"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":[0.0009333056,0.0003002099,0.06346794,0.0003027247,0.0002478193,0.0006416211,0.0012667,0.7339171,0.05116278,0.05870609,0.001203454,0.0878503],"study_design_scores_gemma":[0.00009916112,0.0003289277,0.02825268,0.00004698741,0.0000760589,0.0001745002,0.0001754938,0.9213967,0.007566844,0.03888959,0.002925577,0.00006743705],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9041024,0.0003943725,0.08301183,0.0005685657,0.00004847847,0.00004656424,0.00007653459,0.0001941678,0.011557],"genre_scores_gemma":[0.995738,0.00008634882,0.003780691,0.00002404186,0.00002043358,0.00001518127,0.00001773175,0.000007293785,0.0003102878],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001184401,"threshold_uncertainty_score":0.005078733,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01323231530441154,"score_gpt":0.2312892398477458,"score_spread":0.2180569245433343,"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."}}