{"id":"W2044981414","doi":"10.1016/j.foodqual.2007.03.001","title":"Fusion of sensory and mechanical testing data to define measures of snack food texture","year":2007,"lang":"en","type":"article","venue":"Food Quality and Preference","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Principal component analysis; Artificial intelligence; Pattern recognition (psychology); Computer science; Histogram; Texture (cosmology); Multivariate statistics; Quality (philosophy); Process (computing); Mathematics; Machine learning; Image (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":[0.001780135,0.0008702895,0.0009364876,0.002850052,0.0003098253,0.0008930439,0.0003559785,0.0006771177,0.001098849],"category_scores_gemma":[0.002768712,0.0002639813,0.000570082,0.001907175,0.0003916885,0.001001535,0.0006730055,0.000962571,0.000419854],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002062857,"about_ca_system_score_gemma":0.0003160007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007210214,"about_ca_topic_score_gemma":0.001513795,"domain_scores_codex":[0.9991356,0.0001454739,0.00005707634,0.0001628579,0.0004332338,0.0000658678],"domain_scores_gemma":[0.9979773,0.0005491157,0.000446204,0.0001953825,0.0007066106,0.000125385],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009292918,0.0003797007,0.03774041,0.0002961099,0.0002934595,0.00006767407,0.0002114403,0.001304819,0.8908938,0.0002929911,0.000421119,0.06716917],"study_design_scores_gemma":[0.00007775812,0.002433831,0.5866151,0.00008951066,0.0005625642,0.00161771,0.0008036362,0.04110716,0.360164,0.003654613,0.002673282,0.0002008119],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.800857,0.001837782,0.1895726,0.00017501,0.0001348327,0.0002430255,0.00181269,0.0006432185,0.004723819],"genre_scores_gemma":[0.9508315,0.0007150814,0.04583246,0.0002255186,0.0000566873,0.0001841536,0.001157555,0.00008917899,0.0009079626],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002850052,"threshold_uncertainty_score":0.009414375,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4964160645363944,"score_gpt":0.3862186014129841,"score_spread":0.1101974631234103,"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."}}