{"id":"W4388833021","doi":"10.3390/rs15225412","title":"Using Remote and Proximal Sensing Data and Vine Vigor Parameters for Non-Destructive and Rapid Prediction of Grape Quality","year":2023,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"McMaster University","keywords":"Pearson product-moment correlation coefficient; Normalized Difference Vegetation Index; Mean squared error; Coefficient of determination; Correlation coefficient; Mathematics; Random forest; Statistics; Regression analysis; Linear regression; Support vector machine; Regression; Artificial intelligence; Computer science; Leaf area index; Ecology","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":[0.0009134417,0.0002620096,0.0003899311,0.00009028688,0.0003009678,0.00007582059,0.00008770591,0.0001628487,8.75715e-7],"category_scores_gemma":[0.000367655,0.000233463,0.00004047701,0.000436558,0.0005178801,0.0003378574,0.0004693353,0.0001836723,0.00000131731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008641164,"about_ca_system_score_gemma":0.00001423378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001006431,"about_ca_topic_score_gemma":0.00009234068,"domain_scores_codex":[0.9979213,0.0001455491,0.0004233034,0.0008125848,0.000313059,0.0003841936],"domain_scores_gemma":[0.9987649,0.0002949898,0.0002738099,0.000490437,0.0000405671,0.000135278],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009377826,0.000005036258,0.0006205378,0.0001370192,0.00004729163,0.00001297799,0.0009765312,0.0003463269,0.5465204,0.000001424806,0.00008796856,0.4511507],"study_design_scores_gemma":[0.0005933248,0.00008433499,0.03567058,0.0002327414,0.00009624779,0.0003762023,0.0005509002,0.9538341,0.00726469,0.0009438603,0.0001012808,0.000251745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9736779,0.00003959362,0.02500741,0.0001954231,0.0001742103,0.0006285314,0.00006927862,0.00008287561,0.0001247399],"genre_scores_gemma":[0.6361851,0.0001312446,0.3634511,0.0000441293,0.00007526456,3.555278e-9,0.00006067564,0.00003330267,0.00001919685],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9534878,"threshold_uncertainty_score":0.9520344,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0665312197485446,"score_gpt":0.3018120257510702,"score_spread":0.2352808060025257,"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."}}