{"id":"W2198611818","doi":"","title":"Detecting leafy spurge in native grassland using hyperspectral image analysis","year":2014,"lang":"en","type":"dissertation","venue":"Open ULeth Scholarship (OPUS) (University of Lethbridge)","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Leafy; Hyperspectral imaging; Grassland; Weed; Noxious weed; Environmental science; Geography; Agronomy; Remote sensing; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0001733973,0.0001877833,0.0001437566,0.000617561,0.0001506102,0.0003494033,0.0001062182,0.000180372,0.0003787411],"category_scores_gemma":[0.0001872696,0.00009654644,0.000103675,0.0003274694,0.0001255041,0.00017395,0.0001324944,0.00009176267,0.0001321587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001925509,"about_ca_system_score_gemma":0.0001587399,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01807219,"about_ca_topic_score_gemma":0.06094891,"domain_scores_codex":[0.9999359,0.000006044127,0.000001763523,0.00001418002,0.00003111984,0.00001102972],"domain_scores_gemma":[0.9999332,0.00001749432,0.00001168201,0.00000315247,0.00002620649,0.000008197686],"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":[0.0004790583,0.0003525642,0.1506599,0.0001069386,0.0000674996,0.0002219079,0.000544906,0.004184047,0.7291179,0.00009075212,0.0004690905,0.1137054],"study_design_scores_gemma":[0.0000141161,0.0002161113,0.9176763,0.000009765114,0.0000409059,0.0002338546,0.0005122041,0.03976629,0.04081533,0.00006050633,0.0006390885,0.00001542245],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997591,0.00005408384,0.001607809,0.00001032505,0.000001405132,0.00001182748,0.00005658713,0.00004128258,0.0006255959],"genre_scores_gemma":[0.9877902,0.0001385835,0.01104104,0.00002078803,0.000002533512,0.000008277078,0.0002127338,0.000006193077,0.0007796061],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9819278,"threshold_uncertainty_score":0.03593397,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01817233061443808,"score_gpt":0.262829376815852,"score_spread":0.2446570462014139,"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."}}