{"id":"W4392852573","doi":"10.7717/peerj-cs.1928","title":"Architecting an enterprise financial management model: leveraging multi-head attention mechanism-transformer for user information transformation","year":2024,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"","keywords":"Computer science; Knowledge management; Process management; Finance; Business","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":[0.001305689,0.0005705734,0.0004422315,0.0006670341,0.0004113764,0.001556476,0.001254361,0.0008915072,0.00186154],"category_scores_gemma":[0.002740078,0.0002934397,0.0004681719,0.0004535183,0.0007164826,0.002592094,0.00166149,0.001241303,0.0004812635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008511344,"about_ca_system_score_gemma":0.001491159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003616866,"about_ca_topic_score_gemma":0.004302923,"domain_scores_codex":[0.9994292,0.0002009336,0.00002741655,0.0001394937,0.0001281206,0.00007494306],"domain_scores_gemma":[0.9993511,0.0002951841,0.0000814806,0.00008296477,0.0001182854,0.00007095872],"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":[0.0002350446,0.0003728576,0.008368456,0.0001242802,0.0001159471,0.0004308161,0.0005437066,0.6817039,0.01701023,0.0586328,0.003112949,0.229349],"study_design_scores_gemma":[0.000002348564,0.00001842623,0.0001377761,0.000003668109,0.000006847123,0.00002043339,0.00001837081,0.9923578,0.001102074,0.005843414,0.0004838196,0.000004948986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04790553,0.0001692013,0.946164,0.0006287941,0.0000415191,0.0001004354,0.00006581093,0.0009177818,0.004006982],"genre_scores_gemma":[0.8327574,0.0001998616,0.1633599,0.0002373961,0.0000398446,0.00009474438,0.0001544442,0.00007935002,0.003077141],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003616866,"threshold_uncertainty_score":0.007191658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1042559495177365,"score_gpt":0.3976716681856701,"score_spread":0.2934157186679335,"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."}}