{"id":"W4415718662","doi":"10.1039/d5ay01538c","title":"Construction of prediction models for phenolic compounds in Cabernet Sauvignon grapes based on visible/near-infrared spectroscopy","year":2025,"lang":"en","type":"article","venue":"Analytical Methods","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Agriculture","funders":"Key Technology Research and Development Program of Shandong","keywords":"Partial least squares regression; Smoothing; Principal component analysis; Calibration; Wine; Preprocessor; Chemometrics; Convolution (computer science); Pattern recognition (psychology); Set (abstract data type)","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":[0.0008466299,0.001082533,0.0006036907,0.0008656758,0.0002956322,0.0008471439,0.0005662396,0.0006527848,0.0008605833],"category_scores_gemma":[0.001389972,0.0004207862,0.001062585,0.0003931397,0.0002267217,0.0003574728,0.0004082793,0.0007897459,0.0002767296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009269221,"about_ca_system_score_gemma":0.001022188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0131476,"about_ca_topic_score_gemma":0.01066646,"domain_scores_codex":[0.9997671,0.0000642919,0.00001136043,0.00009038122,0.00003595219,0.00003088921],"domain_scores_gemma":[0.9995933,0.0002648316,0.0000336529,0.00001388869,0.00007552755,0.00001887323],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001791687,0.0001921489,0.00812223,0.000096891,0.0001478566,0.00009922097,0.00005395579,0.9285655,0.007014401,0.0009552321,0.0008651033,0.0537082],"study_design_scores_gemma":[0.000002701483,0.00001671518,0.0005860343,0.00000264062,0.00001269156,0.000005754353,0.000005525496,0.9985066,0.0005770805,0.0001991339,0.00008105948,0.000003966161],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6177417,0.0007497731,0.3766009,0.0003381479,0.00005333494,0.0001268752,0.0008026626,0.001529479,0.002057167],"genre_scores_gemma":[0.952873,0.0002352004,0.04411628,0.00006077444,0.00001554766,0.0001627254,0.001157697,0.00005283428,0.001325962],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0131476,"threshold_uncertainty_score":0.02614212,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02902712923255163,"score_gpt":0.3732038458147598,"score_spread":0.3441767165822082,"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."}}