{"id":"W1607788733","doi":"","title":"Local Lyapunov exponents: Zero plays no role in Forecasting chaotic systems","year":2008,"lang":"en","type":"article","venue":"Cahiers de recherche","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal","funders":"","keywords":"Lyapunov exponent; Corollary; Chaotic; Zero (linguistics); k-nearest neighbors algorithm; Mathematics; Lyapunov function; Computer science; Chaotic systems; Value (mathematics); Applied mathematics; Control theory (sociology); Artificial intelligence; Control (management); Statistics; Nonlinear system; Physics; Discrete mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002321489,0.0002083484,0.0006250448,0.0003337474,0.000153331,0.00005111219,0.0002693654,0.0005215683,0.0002850263],"category_scores_gemma":[0.0006307912,0.0002421055,0.0001839171,0.0006599638,0.0001276949,0.000172789,0.00004669108,0.0007236053,0.0007930919],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001531887,"about_ca_system_score_gemma":0.00005589225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002368377,"about_ca_topic_score_gemma":0.00006850263,"domain_scores_codex":[0.9980158,0.000182168,0.0007757149,0.0004447509,0.00005505269,0.0005265692],"domain_scores_gemma":[0.9988503,0.0002895983,0.0002796735,0.0003823491,0.00004912422,0.0001489583],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006531975,0.001596501,0.3105096,0.001997106,0.002332078,0.001528416,0.06552812,0.05921889,0.00144248,0.5139437,0.01720459,0.02404526],"study_design_scores_gemma":[0.002880688,0.0002994843,0.01031975,0.000343744,0.00004489529,0.0005165761,0.006297057,0.8164292,0.000172146,0.03261725,0.1282293,0.001849922],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6597739,0.01135109,0.1145747,0.0001185848,0.0006250374,0.0005224575,0.0000662555,0.0001169953,0.212851],"genre_scores_gemma":[0.9859552,0.000291554,0.001545359,0.00008256273,0.0001205211,0.00005577759,0.00001521761,0.00004329466,0.01189049],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7572103,"threshold_uncertainty_score":0.9999849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1831588345716816,"score_gpt":0.2635898923230224,"score_spread":0.08043105775134082,"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."}}