{"id":"W2167900190","doi":"10.1046/j.0013-0133.2003.172_5.x","title":"Applied Computational Economics and Finance","year":2003,"lang":"en","type":"article","venue":"The Economic Journal","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":100,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Toolbox; Computational economics; Computer science; MATLAB; Computational finance; Numerical analysis; Process (computing); Theoretical computer science; Algorithm; Computational science; Finance; Mathematical economics; Programming language; Mathematics; Economics; Macroeconomics","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":[0.008320708,0.00009384379,0.0001892355,0.00009505415,0.0003645367,0.0003600965,0.0004250619,0.00003252731,0.000325534],"category_scores_gemma":[0.0006952644,0.00006187821,0.00005906795,0.0000565991,0.0001376412,0.0001440097,0.0000565203,0.000197603,0.0002257661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001053579,"about_ca_system_score_gemma":0.000221988,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001224611,"about_ca_topic_score_gemma":0.000003673849,"domain_scores_codex":[0.9987373,0.0002563629,0.0005096055,0.000207009,0.0001062985,0.0001834012],"domain_scores_gemma":[0.9967405,0.002565792,0.0003787559,0.0002259732,0.00003437852,0.00005457482],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005170087,0.000009032724,0.002216897,4.79926e-7,0.00003586474,0.000001851064,0.0005157135,0.3058234,0.000005587456,0.3742255,0.01058259,0.3065314],"study_design_scores_gemma":[0.0003900568,0.00001516947,0.009772997,0.00000165662,0.000005236273,0.0007148688,0.000192679,0.02811031,0.00002793628,0.8635985,0.09707013,0.0001004145],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9338514,0.0001369921,0.01250311,0.0008401502,0.0008836628,0.00007774521,0.000003662491,0.000006262314,0.05169703],"genre_scores_gemma":[0.9671543,0.0001024489,0.03136826,0.000343079,0.0001927953,0.000002597144,1.661751e-7,0.00001027711,0.0008260232],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4893731,"threshold_uncertainty_score":0.3564369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0775707606853222,"score_gpt":0.3474639439196236,"score_spread":0.2698931832343014,"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."}}