{"id":"W2281627593","doi":"10.1139/cjps-2015-0120","title":"Using remote sensing to understand Pinot noir vineyard variability in Ontario","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Plant Science","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Brock University","funders":"","keywords":"Vineyard; Vine; Veraison; Normalized Difference Vegetation Index; Multispectral image; Environmental science; Water content; Sampling (signal processing); Growing season; Vegetation (pathology); Remote sensing; Berry; Horticulture; Agronomy; Geography; Geology; Leaf area index; Biology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001904279,0.0001571708,0.0001506791,0.0004875428,0.0005055508,0.0006396442,0.0003162638,0.0001280189,0.000650752],"category_scores_gemma":[0.0006139363,0.0001613048,0.0001470042,0.0007248997,0.0001939047,0.0002331783,0.0002594218,0.0001080492,0.00007985282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004703598,"about_ca_system_score_gemma":0.001811629,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9114215,"about_ca_topic_score_gemma":0.9713996,"domain_scores_codex":[0.9998354,0.00001050239,0.000005471421,0.00006070446,0.00004765502,0.00004019417],"domain_scores_gemma":[0.9997206,0.00004257183,0.0000688084,0.00001752742,0.0001094836,0.00004100333],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001034371,0.00002553658,0.9712921,0.00002762069,0.00004689489,0.0001588331,0.002248371,0.0009238534,0.01203641,0.0002029426,0.000448168,0.01248586],"study_design_scores_gemma":[9.395341e-7,0.00000375651,0.9984661,0.000002184643,0.000003446697,0.0000109437,0.0002948575,0.0005804181,0.00009071234,0.00001002506,0.0005346241,0.000001929899],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974561,0.0000779326,0.0002332899,0.00002366201,0.000001686622,0.000009535293,0.0003176663,0.000005807496,0.001874329],"genre_scores_gemma":[0.9983445,0.00005421546,0.0002645373,0.000007395044,0.00000107186,0.000004830788,0.0004261628,0.000004948017,0.0008922619],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08857846,"threshold_uncertainty_score":0.1782002,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02956185941826484,"score_gpt":0.2164000205145589,"score_spread":0.1868381610962941,"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."}}