{"id":"W4323665544","doi":"10.3390/rs15061497","title":"Assessing the Leaf Blade Nutrient Status of Pinot Noir Using Hyperspectral Reflectance and Machine Learning Models","year":2023,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Horticultural and Viticultural Research","field":"Agricultural and Biological Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"McMaster University","keywords":"Hyperspectral imaging; Mean squared error; Spectroradiometer; Partial least squares regression; Support vector machine; Random forest; Feature selection; Linear regression; Mathematics; Remote sensing; Nutrient; Environmental science; Computer science; Reflectivity; Statistics; Artificial intelligence; Chemistry; Geography; Optics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003637045,0.0005456461,0.0003634667,0.0003449352,0.0001997933,0.0006836714,0.0003342397,0.0003392206,0.0003667586],"category_scores_gemma":[0.0005094864,0.0002399448,0.000331538,0.0002822514,0.0001219544,0.0003869637,0.0001886357,0.0002378325,0.0001767394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005315145,"about_ca_system_score_gemma":0.0002790156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02759235,"about_ca_topic_score_gemma":0.05491498,"domain_scores_codex":[0.999848,0.00002433563,0.000005612873,0.00007916956,0.00003077506,0.00001205767],"domain_scores_gemma":[0.9998568,0.00006080026,0.00002888482,0.00001033415,0.00003350125,0.000009689736],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007358835,0.0003504663,0.3673539,0.0003490355,0.0002946376,0.0007295363,0.0007865374,0.1824566,0.352616,0.0003502245,0.0005857387,0.09339159],"study_design_scores_gemma":[0.000008147902,0.000173005,0.3845829,0.00001583994,0.00004798292,0.0001367777,0.0003037179,0.602322,0.01170352,0.0001652002,0.0005040876,0.00003688682],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9930236,0.0001204448,0.006069215,0.00002217178,0.000005154064,0.0000142324,0.000118717,0.00004604399,0.0005803017],"genre_scores_gemma":[0.9926372,0.00009475385,0.006208252,0.00001152162,0.000002180495,0.000008821924,0.000280433,0.000008033526,0.0007487431],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02759235,"threshold_uncertainty_score":0.05486351,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1460508056988861,"score_gpt":0.3410792778720356,"score_spread":0.1950284721731495,"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."}}