{"id":"W2075882365","doi":"10.1016/j.foodqual.2015.02.015","title":"Product selection for liking studies: The sensory informed design","year":2015,"lang":"en","type":"article","venue":"Food Quality and Preference","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Universities’ Application Centre; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Product (mathematics); Product design; Computer science; Sensory system; Variety (cybernetics); Selection (genetic algorithm); Imputation (statistics); Marketing; Cognitive psychology; Psychology; Mathematics; Machine learning; Artificial intelligence; Business; Missing data","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":[0.04993175,0.001790261,0.00275856,0.002115065,0.001030892,0.002375918,0.002525531,0.00189881,0.007951893],"category_scores_gemma":[0.06874872,0.001076886,0.002295825,0.002407699,0.002277885,0.001268705,0.002266142,0.002659568,0.001331006],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001421809,"about_ca_system_score_gemma":0.002614184,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004091905,"about_ca_topic_score_gemma":0.0005891414,"domain_scores_codex":[0.937012,0.04733928,0.00301873,0.003948752,0.007920251,0.0007610152],"domain_scores_gemma":[0.9592509,0.02412651,0.003286988,0.007370763,0.005247598,0.0007172301],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.03231952,0.006695096,0.01197865,0.004978512,0.00215342,0.0002055966,0.002402008,0.03873342,0.03037546,0.07415085,0.005555301,0.7904522],"study_design_scores_gemma":[0.01996405,0.1153911,0.05845971,0.001717133,0.003592347,0.0005878671,0.0016988,0.3413455,0.05954245,0.2505022,0.1460504,0.001148417],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04896764,0.0003784684,0.9153479,0.0002674343,0.0003281996,0.02851378,0.0007848601,0.0006054839,0.004806146],"genre_scores_gemma":[0.1246358,0.0003822442,0.8106961,0.0004653307,0.0001002237,0.0610134,0.0004972052,0.0001667124,0.002042942],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04993175,"threshold_uncertainty_score":0.2640675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.776046502817835,"score_gpt":0.4516789985044561,"score_spread":0.3243675043133789,"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."}}