{"id":"W4225402687","doi":"10.3390/books978-3-0365-4079-5","title":"Implementation of Artificial Intelligence in Food Science, Food Quality, and Consumer Preference Assessment","year":2022,"lang":"en","type":"book","venue":"","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Regional Development Fund; Ministerio de Economía y Competitividad; Universidad Panamericana; Australian Government; Canadian Institute for Theoretical Astrophysics","keywords":"Food quality; Wine; Food processing; Quality (philosophy); Engineering; Emerging technologies; Business; Marketing; Computer science; Food science; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001384415,0.0001775954,0.0002796679,0.0002863889,0.00003320438,0.00001846532,0.0003012426,0.0001051933,0.0002688002],"category_scores_gemma":[0.00003312901,0.0001859477,0.00002497773,0.0002609466,0.0003337615,0.0001056669,0.0002744792,0.000382612,0.000001003751],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004240308,"about_ca_system_score_gemma":0.00007349954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001175572,"about_ca_topic_score_gemma":0.0002698857,"domain_scores_codex":[0.9985879,0.000009228949,0.0005215374,0.000295964,0.0003578332,0.0002275149],"domain_scores_gemma":[0.9994759,0.000113073,0.0001014438,0.000237307,0.00004170613,0.00003060152],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000007135869,0.00004561473,0.0005502577,0.0005805327,0.00007663773,0.00000167983,0.0002043763,0.001756459,0.07002711,0.6375088,0.0002250211,0.2890164],"study_design_scores_gemma":[0.0001588251,0.0004702245,0.0009540577,0.00007877269,0.00002231087,0.000002934207,0.002469788,0.001069408,0.6308452,0.3601294,0.003047455,0.0007516079],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.604659,0.001720581,0.04148681,0.0001351288,0.0007010751,0.002815611,0.0007425481,0.001803174,0.3459361],"genre_scores_gemma":[0.9931869,0.0002641137,0.005731152,0.000007950615,0.000008562785,0.00006036006,0.00002588429,0.00002480041,0.0006902634],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5608181,"threshold_uncertainty_score":0.7582728,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09493028660701139,"score_gpt":0.364523730768569,"score_spread":0.2695934441615576,"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."}}