{"id":"W4389301985","doi":"10.1139/dsa-2023-0024","title":"Detecting cool-climate Riesling vineyard variation using unmanned aerial vehicles and proximal sensors","year":2023,"lang":"en","type":"article","venue":"Drone Systems and Applications","topic":"Horticultural and Viticultural Research","field":"Agricultural and Biological Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Brock University","funders":"Ontario Ministry of Food and Agriculture","keywords":"Vineyard; Vine; Normalized Difference Vegetation Index; Viticulture; Berry; Environmental science; Remote sensing; Vegetation (pathology); Multispectral image; Pruning; Horticulture; Agronomy; Geography; Wine; Leaf area index; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"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":[0.0001003854,0.0002245529,0.0001777158,0.0002863076,0.0001456794,0.000303063,0.000201915,0.0001288232,0.0005208709],"category_scores_gemma":[0.00019337,0.0001023249,0.0001100032,0.0002644382,0.0001307254,0.0002383941,0.0002838119,0.000125437,0.0001236695],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003246648,"about_ca_system_score_gemma":0.0002005204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02075486,"about_ca_topic_score_gemma":0.07352233,"domain_scores_codex":[0.9998606,0.00001401522,0.000002281322,0.00005707779,0.00004584973,0.00002012317],"domain_scores_gemma":[0.9999077,0.00001356679,0.00002719134,0.00001140136,0.00002731427,0.00001283558],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0003648939,0.0001181479,0.3742014,0.000119883,0.0001180375,0.0001778097,0.0009718156,0.008075623,0.4521275,0.0004752571,0.0009597811,0.1622898],"study_design_scores_gemma":[0.00001713851,0.0002033709,0.9546905,0.00001220771,0.00003084723,0.0001416955,0.0004954448,0.02571645,0.01600623,0.0001765556,0.002487916,0.00002169446],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9888029,0.00009371273,0.009238919,0.00001774862,0.000006923344,0.00001733579,0.000182348,0.00009943241,0.001540698],"genre_scores_gemma":[0.989759,0.00005712256,0.00893218,0.00001531251,0.000003435813,0.000009657855,0.0002224601,0.000008776385,0.0009921398],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02075486,"threshold_uncertainty_score":0.04126805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04215913979884339,"score_gpt":0.281330493699364,"score_spread":0.2391713539005206,"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."}}