{"id":"W3029838451","doi":"10.5267/j.dsl.2020.5.004","title":"An approach based on machine learning techniques for forecasting Vietnamese consumers’ purchase behaviour","year":2020,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Vietnamese; Artificial intelligence; Machine learning; Computer science; Marketing; Business","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.007205779,0.0002993459,0.0003864949,0.0006999762,0.001269132,0.0009542721,0.002747389,0.00009553814,0.00006809766],"category_scores_gemma":[0.01085354,0.0002266956,0.0002120753,0.003112059,0.0007084334,0.0006926021,0.0002326831,0.0004301936,0.00004167396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008667779,"about_ca_system_score_gemma":0.000123301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001963258,"about_ca_topic_score_gemma":0.000001502676,"domain_scores_codex":[0.9942651,0.0001183976,0.0008711366,0.001584476,0.002536625,0.000624266],"domain_scores_gemma":[0.9959578,0.001437467,0.000449546,0.001031023,0.0005778061,0.0005463379],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004269409,0.0004143307,0.01810789,0.00001252885,0.000004901248,0.00002381476,0.001375199,0.01298421,0.1156356,0.001582858,0.01171614,0.8377156],"study_design_scores_gemma":[0.0003719432,0.0004835171,0.0005122212,0.0000285997,0.000009916047,0.00001135673,0.0003105188,0.9728777,0.01143261,0.00122819,0.01238628,0.0003470949],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.178493,0.000006330715,0.8106979,0.009055186,0.00006905042,0.00083694,0.00005261193,0.0004096031,0.0003794294],"genre_scores_gemma":[0.6159173,6.016995e-7,0.3755438,0.008285747,0.00005377076,0.0001510313,0.00001540717,0.00002298329,0.00000934266],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9598935,"threshold_uncertainty_score":0.9974785,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1784979076025925,"score_gpt":0.3992268747128872,"score_spread":0.2207289671102947,"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."}}