{"id":"W4311938511","doi":"10.32920/21689399","title":"Service brand and customer attire: A genetic algorithm approach","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Consumer Behavior in Brand Consumption and Identification","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Clothing; Marketing; Service (business); Business; Advertising; Perception; Conceptual model; Order (exchange); Empirical research; Clothing industry; Brand management; Brand awareness; Personality; Psychology; Computer science; Social psychology; Political science; Mathematics","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":[0.001432312,0.0005880861,0.000753388,0.001643556,0.0005720641,0.001220587,0.001372523,0.001436664,0.002787977],"category_scores_gemma":[0.004255494,0.0003590093,0.0007026785,0.001526965,0.0007119027,0.0008893171,0.0006947764,0.0009033883,0.000247343],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001245176,"about_ca_system_score_gemma":0.001567086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01480201,"about_ca_topic_score_gemma":0.01039732,"domain_scores_codex":[0.9996165,0.0002061713,0.00001467301,0.00004859936,0.00006812566,0.00004586539],"domain_scores_gemma":[0.9990134,0.0006811713,0.0000543267,0.00002861473,0.0001915167,0.00003091318],"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":[0.00003263728,0.00008755106,0.002928615,0.00003546861,0.00007999853,0.00006400218,0.0001051909,0.92755,0.0003945091,0.01319234,0.000803742,0.05472593],"study_design_scores_gemma":[0.00000890533,0.00001846428,0.0001881773,0.000007925854,0.00001389067,0.00001070698,0.00002224053,0.9962268,0.00006234423,0.003136267,0.0003002242,0.00000406223],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1061337,0.0006684698,0.8757779,0.001319799,0.00009488308,0.0002762782,0.0001060963,0.0003571682,0.01526575],"genre_scores_gemma":[0.5696073,0.0006348855,0.422063,0.0004049898,0.00007241131,0.0005557769,0.0002018531,0.00007915033,0.006380679],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01480201,"threshold_uncertainty_score":0.0294317,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03853630808355243,"score_gpt":0.2535507298394436,"score_spread":0.2150144217558912,"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."}}