{"id":"W4409416296","doi":"10.1016/j.jfca.2025.107630","title":"Food quality assessment and quantification using multispectral images from screen and smartphone for kiwifruit ripeness and rice discrimination","year":2025,"lang":"en","type":"article","venue":"Journal of Food Composition and Analysis","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Ripeness; Kiwi; Multispectral image; Quality assessment; Food science; Biology; Geography; Remote sensing; Business; Ripening","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":[0.0002761382,0.0004363269,0.0002933659,0.0007225117,0.0002018271,0.000389108,0.0002847714,0.0005591724,0.001381423],"category_scores_gemma":[0.0002772773,0.0001976958,0.0003741579,0.0004960908,0.0001605379,0.0003211189,0.0003085542,0.0003548965,0.0005120729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001656302,"about_ca_system_score_gemma":0.0001706792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002667707,"about_ca_topic_score_gemma":0.009165039,"domain_scores_codex":[0.9998161,0.0000147246,0.000006451552,0.00006265155,0.00007648584,0.00002353263],"domain_scores_gemma":[0.9998525,0.00002571872,0.00002939518,0.00001019821,0.000071179,0.00001097484],"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":[0.0005041787,0.0001197959,0.01624377,0.00026733,0.00007517519,0.0001459477,0.0001507296,0.0004020919,0.9331451,0.0001174027,0.0005937024,0.04823481],"study_design_scores_gemma":[0.0000419184,0.0008334498,0.3430331,0.00004515788,0.000353005,0.001409381,0.0006997783,0.04793662,0.5994979,0.0003212869,0.005664389,0.0001639418],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9394507,0.0013969,0.05436958,0.0001096835,0.00005949398,0.00009645028,0.001135082,0.0004971646,0.002885019],"genre_scores_gemma":[0.9252111,0.0008624358,0.06770366,0.000222174,0.0000244807,0.0001237322,0.000802092,0.00006441297,0.00498587],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002667707,"threshold_uncertainty_score":0.005304396,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05328819642378099,"score_gpt":0.3759805464821234,"score_spread":0.3226923500583424,"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."}}