{"id":"W4406290668","doi":"10.5267/j.ac.2024.10.002","title":"Exploring the evolution of scientific publication on portfolio optimization in the light of artificial intelligence: A bibliometric study","year":2025,"lang":"en","type":"article","venue":"Accounting","topic":"Electric Power System Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Portfolio; Field (mathematics); Data science; Computer science; Transformative learning; Context (archaeology); Management science; Bibliometrics; Artificial intelligence; Knowledge management; Sociology; Data mining; Engineering","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":["bibliometrics"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.002320903,0.00009449001,0.0001330609,0.0135128,0.000116695,0.0001537574,0.000369641,0.00003427023,0.000006427083],"category_scores_gemma":[0.0006258577,0.00006880391,0.00003269918,0.07328966,0.00002402192,0.0005379791,0.00003008245,0.0001338181,0.000002657772],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001591423,"about_ca_system_score_gemma":0.00005187002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007258284,"about_ca_topic_score_gemma":0.00002121987,"domain_scores_codex":[0.9985676,0.00008594905,0.0006102116,0.0001698151,0.0004028224,0.0001635832],"domain_scores_gemma":[0.9989541,0.0001847261,0.0001730486,0.0003764551,0.0003043138,0.00000735396],"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":[0.000009069709,0.0001947177,0.01028945,0.00007391991,0.00002781385,2.933876e-7,0.001831231,0.958519,0.001025,0.006705774,0.0001772642,0.0211464],"study_design_scores_gemma":[0.0001141102,0.00006331185,0.03406925,0.0001518448,0.00004316009,9.751265e-7,0.005392691,0.9531932,0.006515083,0.0002461119,0.00007660833,0.0001335963],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.807959,0.000231975,0.1855968,0.0001233394,0.0007700141,0.000835099,0.000001100138,0.00007181293,0.004410842],"genre_scores_gemma":[0.9996004,0.00001340576,0.0002194361,0.000005849555,0.00003170692,0.00009927954,0.000005430635,0.00001016467,0.00001440191],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1916413,"threshold_uncertainty_score":0.9976682,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03909395225912605,"score_gpt":0.2645421556346679,"score_spread":0.2254482033755418,"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."}}