{"id":"W4366535444","doi":"10.5267/j.ac.2023.3.004","title":"latent Dirichlet allocation method-based nowcasting approach for prediction of silver price","year":2023,"lang":"en","type":"article","venue":"Accounting","topic":"Identification and Quantification in Food","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Latent Dirichlet allocation; Autoregressive integrated moving average; Computer science; Random forest; Nowcasting; Support vector machine; Econometrics; Estimation; Regression analysis; Regression; Benchmark (surveying); Data mining; Statistics; Artificial intelligence; Machine learning; Time series; Mathematics; Topic model; Geography; Economics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003226668,0.0008223535,0.001448016,0.00198186,0.0007852105,0.00147854,0.001500415,0.001572893,0.003293944],"category_scores_gemma":[0.005795126,0.0006072072,0.001808894,0.001362669,0.0006672849,0.002042956,0.0008491222,0.002267136,0.0008533738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001238364,"about_ca_system_score_gemma":0.001619787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01783207,"about_ca_topic_score_gemma":0.01430232,"domain_scores_codex":[0.9986481,0.0005913044,0.00008819276,0.0003294823,0.0001707823,0.0001720225],"domain_scores_gemma":[0.9972382,0.002086887,0.0001550889,0.00009210602,0.0003525912,0.00007510868],"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":[0.0004766671,0.0002451712,0.008701267,0.0001921248,0.0002408126,0.0002321932,0.0003831136,0.8033678,0.002685959,0.02476733,0.003700675,0.1550069],"study_design_scores_gemma":[0.000007025074,0.00001197065,0.0003829229,0.000006836249,0.00001167193,0.000009577472,0.00001782265,0.9955052,0.0002038773,0.003535775,0.0002968876,0.00001037102],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06786341,0.001055305,0.9262937,0.0007421809,0.0002806142,0.000109499,0.0003365176,0.0005623527,0.002756409],"genre_scores_gemma":[0.8320714,0.001202968,0.1532567,0.0002341419,0.000532123,0.0003296475,0.001186337,0.0001511006,0.01103553],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01783207,"threshold_uncertainty_score":0.03545654,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04794780102728456,"score_gpt":0.304922199300108,"score_spread":0.2569743982728234,"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."}}