{"id":"W7104269233","doi":"10.23977/acss.2025.090316","title":"Study on the Nonlinear Causal Impact of Investor Sentiment on Futures Pricing Efficiency: Based on Generalized Random Forest and Dual Machine Learning Methods","year":2025,"lang":"","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Futures contract; Dual (grammatical number); Empirical evidence; Inference; Empirical research; Index (typography); Random forest","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003157917,0.0004782032,0.00128422,0.0005609192,0.0003876876,0.0002903424,0.0001998919,0.0001199636,0.00001112755],"category_scores_gemma":[0.0001294501,0.0003407579,0.0001761841,0.000457649,0.0001699924,0.0001784566,0.0001099697,0.0004033466,0.000001326884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001150885,"about_ca_system_score_gemma":0.00007269316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005796317,"about_ca_topic_score_gemma":0.00004081697,"domain_scores_codex":[0.9965179,0.0008440269,0.001317414,0.0007782639,0.0001399609,0.000402423],"domain_scores_gemma":[0.9970162,0.001765848,0.0007579647,0.000335568,0.0000471897,0.00007723535],"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":[0.00113839,0.001059196,0.1238027,0.0003499662,0.0002516212,0.00001789278,0.001591778,0.8363329,0.00003599651,0.02837237,0.00006362021,0.006983595],"study_design_scores_gemma":[0.00451825,0.005142037,0.03816666,0.00082062,0.00002976764,0.000001463399,0.000316043,0.9488696,0.00003979563,0.0008215574,0.0009231688,0.0003509979],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9376202,0.02478962,0.03285847,0.0001827639,0.001695672,0.001776293,0.00006606288,0.00001521499,0.0009957092],"genre_scores_gemma":[0.9967056,0.001678873,0.000969643,0.0002608688,0.0002130493,0.00006765989,0.00000725061,0.0000230759,0.00007399657],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1125368,"threshold_uncertainty_score":0.9999045,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03186792894287196,"score_gpt":0.311659912239812,"score_spread":0.27979198329694,"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."}}