{"id":"W6931863186","doi":"10.5683/sp2/wwyewk","title":"EaPd-7 and EaPd-8 -- Lethbridge County -- UAV Multispectral -- NDVI -- 2018","year":2020,"lang":"en","type":"dataset","venue":"Borealis","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Normalized Difference Vegetation Index; Multispectral image; Vegetation (pathology); Multispectral Scanner; Sequoia; Data set; Multispectral pattern recognition","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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":[0.0007463835,0.002139914,0.001150573,0.002134146,0.0006572824,0.001168048,0.002400791,0.001229892,0.01426578],"category_scores_gemma":[0.001529343,0.0004585856,0.0009087138,0.003342449,0.0004405082,0.001049044,0.0009371887,0.001127882,0.0264229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001103619,"about_ca_system_score_gemma":0.001618486,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06036863,"about_ca_topic_score_gemma":0.1101865,"domain_scores_codex":[0.9993279,0.0000675622,0.00004485534,0.0002059025,0.0002236125,0.000130164],"domain_scores_gemma":[0.9992447,0.00007707791,0.00006078935,0.0001937616,0.0003104543,0.0001133326],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001728244,0.00009915972,0.003454299,0.0003787874,0.00005435349,0.00006329739,0.00004578647,0.001083125,0.0007195777,0.0003685929,0.9872456,0.006314527],"study_design_scores_gemma":[0.0002652253,0.00005786822,0.02644021,0.0001907812,0.00004369131,0.0001511253,0.0002224818,0.00277346,0.002044077,0.0008935065,0.9668461,0.00007160609],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001492679,0.00008354707,0.0001618205,0.00005518797,0.00005643694,0.00002279832,0.9962213,0.0007287057,0.001177469],"genre_scores_gemma":[0.0009000051,0.00002064756,0.0002986494,0.00001772534,0.00000597205,0.00002428033,0.9982888,0.00004524347,0.0003985078],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9396313,"threshold_uncertainty_score":0.1200345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02392418092892093,"score_gpt":0.2701794058642034,"score_spread":0.2462552249352824,"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."}}