{"id":"W3039621746","doi":"10.3390/foods9070873","title":"Multiple Correspondence and Hierarchical Cluster Analyses for the Profiling of Fresh Apple Customers Using Data from Two Marketplaces","year":2020,"lang":"en","type":"article","venue":"Foods","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Agriculture and Agri-Food Canada","funders":"Agriculture and Agri-Food Canada","keywords":"Cultivar; Profiling (computer programming); Marketing; Market segmentation; Dimension (graph theory); Advertising; Business; Mathematics; Biology; Computer science; Horticulture","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004139162,0.000090847,0.0002144895,0.000005966793,0.00016231,0.00004408694,0.0003459828,0.00003852326,0.0001539133],"category_scores_gemma":[0.001251042,0.00002997286,0.00005726156,0.0002218559,0.0001361272,0.0000684438,0.0001892546,0.00008962355,8.45657e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002230034,"about_ca_system_score_gemma":0.000005686607,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003795329,"about_ca_topic_score_gemma":0.0002846455,"domain_scores_codex":[0.9989464,0.0002399241,0.000207545,0.0003186962,0.0001460143,0.0001414477],"domain_scores_gemma":[0.9935839,0.006120638,0.00008245146,0.00009548545,0.00003531437,0.00008218084],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001611676,0.00005556417,0.03531136,0.00003946818,0.0002801206,0.00000301618,0.0002735131,0.001512369,0.8533576,0.0003498683,0.001166589,0.1060389],"study_design_scores_gemma":[0.0002407294,0.0001256111,0.008678368,0.00001058541,0.0001666499,7.436689e-7,0.0009509727,0.9774306,0.007165253,0.0002741142,0.004838764,0.000117539],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9661298,0.0004365474,0.0299835,0.001001475,0.00005281641,0.0002442852,0.002100111,0.00001458301,0.00003687002],"genre_scores_gemma":[0.9426397,0.00001841411,0.05665328,0.0002806832,0.0002189391,0.000004155012,0.000172296,9.923644e-7,0.00001153616],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9759183,"threshold_uncertainty_score":0.1685243,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.309842443548665,"score_gpt":0.4083396929827565,"score_spread":0.0984972494340915,"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."}}