{"id":"W4414406028","doi":"10.1109/icctdc64446.2025.11158817","title":"GenAI for Investment Recommendations Using RSS Feed","year":2025,"lang":"en","type":"article","venue":"","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"RSS; Pipeline (software); Categorization; Natural language; Investment (military); Simple (philosophy)","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":[0.001143802,0.00130563,0.0006392956,0.003943989,0.0004508503,0.002083336,0.0009536134,0.0009459836,0.01338019],"category_scores_gemma":[0.005262604,0.0004360221,0.0004787764,0.002199362,0.0001022284,0.00167788,0.0007329712,0.000811531,0.01081399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00049519,"about_ca_system_score_gemma":0.0006044628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006284337,"about_ca_topic_score_gemma":0.01673797,"domain_scores_codex":[0.9992472,0.0001535739,0.0000518631,0.0001277345,0.0003645055,0.00005524532],"domain_scores_gemma":[0.9979746,0.0009150389,0.0001389055,0.000228387,0.0006502269,0.00009280311],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001209449,0.0006229895,0.01820234,0.0008678181,0.0002504632,0.0009211869,0.0006277194,0.01623622,0.01741733,0.003546929,0.1351731,0.8049244],"study_design_scores_gemma":[0.0003140644,0.0007116116,0.01758964,0.0003027491,0.0003530921,0.0005775832,0.001455913,0.6750241,0.04646747,0.009220582,0.2477596,0.0002236875],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1347976,0.002902882,0.4633598,0.003027756,0.001193279,0.002134752,0.03620737,0.2460915,0.110285],"genre_scores_gemma":[0.4767507,0.001902711,0.4272389,0.0007978887,0.0004520987,0.0008237913,0.03797354,0.002009345,0.05205103],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01338019,"threshold_uncertainty_score":0.04476124,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3362675229808113,"score_gpt":0.5208388340384507,"score_spread":0.1845713110576394,"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."}}