{"id":"W2069152178","doi":"10.1145/1569901.1570119","title":"Soft memory for stock market analysis using linear and developmental genetic programming","year":2009,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Genetic programming; Long memory; Profit (economics); Econometrics; Parallel computing; Economics; Microeconomics; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.00009600449,0.00006797386,0.00008540957,0.0000859367,0.0002030633,0.00005895519,0.0001661973,0.00002311256,0.00001273212],"category_scores_gemma":[0.00000654265,0.00006285219,0.00004402166,0.0004528536,0.00001650758,0.0001368467,0.00005040754,0.00002562665,0.000001310015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002478296,"about_ca_system_score_gemma":0.00004253075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008615957,"about_ca_topic_score_gemma":0.000005013721,"domain_scores_codex":[0.9993936,0.000008265726,0.0001269128,0.0002383079,0.00008511453,0.0001477533],"domain_scores_gemma":[0.9997102,0.00003550304,0.00003011429,0.0001245632,0.00004197745,0.00005761872],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004533502,0.000167386,0.002597211,0.00001098803,0.0001563474,0.000002381016,0.0003500332,0.002099647,0.0008185331,0.003385439,0.0008548001,0.9895527],"study_design_scores_gemma":[0.0001143587,0.00002749003,0.02253467,0.000001630823,0.00004008758,0.0000128902,0.00003587848,0.9747412,0.00008784389,0.0006144196,0.001682203,0.0001073637],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03250301,0.0000825298,0.9665547,0.0003092367,0.00001160136,0.00021536,0.00000133977,0.00006360695,0.0002586398],"genre_scores_gemma":[0.09446853,0.000002886265,0.9048765,0.0001505685,0.0000293764,0.00001858301,0.000002566125,0.000002155887,0.0004488614],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9894453,"threshold_uncertainty_score":0.2563038,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02114675253959315,"score_gpt":0.2716016200244956,"score_spread":0.2504548674849024,"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."}}