{"id":"W4409603233","doi":"10.61091/jcmcc127b-137","title":"Design of Computational Identification Model and Optimization Strategy for Consumer Demand Based on Multimodal Data Fusion","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"E-commerce and Technology Innovations","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Identification (biology); Computer science; Fusion; Machine learning; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.001492054,0.000165349,0.0003911329,0.0004597309,0.0002803144,0.0001868891,0.000324907,0.000139597,0.000001844099],"category_scores_gemma":[0.0005708679,0.0001564714,0.00004246613,0.0003628904,0.0001164814,0.0004084726,0.0001760087,0.0001860375,2.710067e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002121214,"about_ca_system_score_gemma":0.0001103328,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000691179,"about_ca_topic_score_gemma":2.487276e-7,"domain_scores_codex":[0.9984928,0.00002193515,0.0008851119,0.0001961708,0.0002674219,0.0001365128],"domain_scores_gemma":[0.9970759,0.0006646899,0.001085649,0.0002348767,0.0009252863,0.00001355422],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001306672,0.0003588849,0.0001424945,0.0002803467,0.00005149957,5.407492e-7,0.00002285925,0.4552894,0.0001997481,0.5420548,0.0003872738,0.001081541],"study_design_scores_gemma":[0.002523843,0.00006311314,0.00005950134,0.0001811567,0.0001109829,0.00000112285,0.00006393085,0.681061,0.00004890143,0.3157758,0.00002351872,0.00008717478],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08792619,0.00005516616,0.9094031,0.0004662294,0.001555682,0.0004442357,0.000007017327,0.00002563967,0.000116702],"genre_scores_gemma":[0.9551449,0.00000761452,0.04450416,0.00008941349,0.0002019246,0.000003259044,0.00003169478,0.00001499639,0.000002082372],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8672187,"threshold_uncertainty_score":0.638072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04192391684792528,"score_gpt":0.2897941010721923,"score_spread":0.247870184224267,"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."}}