{"id":"W4389584966","doi":"10.17118/11143/20943","title":"Trends in consumer preferences for product customization and theirapplication in product design","year":2023,"lang":"en","type":"article","venue":"","topic":"Color perception and design","field":"Psychology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Personalization; Product (mathematics); Product design; Computer science; Mass customization; Manufacturing engineering; Business; Engineering; World Wide Web; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003648203,0.0001459994,0.0001568133,0.00135735,0.0002548212,0.0009258104,0.0002089024,0.000324679,0.001686247],"category_scores_gemma":[0.01285076,0.000119272,0.0002800443,0.001371879,0.0002907418,0.0009234683,0.0003768757,0.0004653779,0.0001806908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005921309,"about_ca_system_score_gemma":0.0002352681,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001514428,"about_ca_topic_score_gemma":0.002093543,"domain_scores_codex":[0.9983634,0.0005841715,0.0001424085,0.0001532832,0.0006761132,0.000080545],"domain_scores_gemma":[0.9831321,0.008816824,0.003627379,0.0007204424,0.003308949,0.0003942909],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005196257,0.0002239277,0.928907,0.0001691924,0.00007984266,0.00007480979,0.003642907,0.0007974131,0.004685325,0.0003721746,0.0004490412,0.06007874],"study_design_scores_gemma":[0.000006317621,0.0003936168,0.9913763,0.00002495681,0.00002152701,0.0001287594,0.003394638,0.002335762,0.00123123,0.0002744738,0.000795415,0.00001699476],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967012,0.00009884044,0.001186568,0.0000526554,0.000003061713,0.00002943398,0.0000887644,0.00001071539,0.001828722],"genre_scores_gemma":[0.998561,0.00004446046,0.001085034,0.00001193548,0.000002196043,0.00001474465,0.0000811852,0.000002696317,0.0001969418],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003648203,"threshold_uncertainty_score":0.01929379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1445126230529622,"score_gpt":0.3789321179738772,"score_spread":0.2344194949209149,"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."}}