{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002393259,0.0005375594,0.00074331,0.0006166891,0.000552982,0.001552673,0.0007662863,0.0007332839,0.0009200164],"category_scores_gemma":[0.01543009,0.0001800251,0.0002996966,0.0005273344,0.001659695,0.003544012,0.001135968,0.001309729,0.0002004969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004527225,"about_ca_system_score_gemma":0.0005540575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001378359,"about_ca_topic_score_gemma":0.001156785,"domain_scores_codex":[0.9995539,0.0001489768,0.00003333907,0.0001447225,0.00007818406,0.00004091127],"domain_scores_gemma":[0.9950345,0.002869185,0.0007337513,0.0007257349,0.0004308683,0.0002061024],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004939049,0.0000708283,0.04711797,0.000611263,0.0002922257,0.0004255362,0.001240275,0.1848708,0.01498809,0.3698559,0.002364038,0.3776692],"study_design_scores_gemma":[0.00008304333,0.0004685633,0.01598288,0.0002317597,0.0001297474,0.000283604,0.0004334262,0.5547973,0.01066738,0.4123076,0.004486227,0.000128486],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4216974,0.003082032,0.5626081,0.003226102,0.0001423987,0.00004695748,0.0001213533,0.0002565846,0.008819004],"genre_scores_gemma":[0.9778624,0.0003060969,0.02101097,0.00005640525,0.00006189147,0.00001112478,0.00002538863,0.00001795646,0.0006477353],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002393259,"threshold_uncertainty_score":0.01265693,"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."}}