{"id":"W887479332","doi":"","title":"Quantifying Fractional Ground Cover on the Climate Sensitive High Plains Using AVIRIS and Landsat TM Data","year":2013,"lang":"en","type":"book","venue":"NASA STI Repository (National Aeronautics and Space Administration)","topic":"Botany and Plant Ecology Studies","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Vegetation (pathology); Remote sensing; Imaging spectrometer; Endmember; Multispectral image; Environmental science; Thematic Mapper; Geology; Physical geography; Geography; Hyperspectral imaging; Satellite imagery; Spectrometer","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":[0.0002891818,0.0002186978,0.0001359777,0.001380188,0.0001912908,0.0005175713,0.0001706239,0.000209009,0.0003836798],"category_scores_gemma":[0.0007397125,0.0001079258,0.0001802846,0.0008330045,0.0001565596,0.0004421672,0.0002384394,0.0001061518,0.0001199246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003904296,"about_ca_system_score_gemma":0.0002087268,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02899083,"about_ca_topic_score_gemma":0.06215295,"domain_scores_codex":[0.9998075,0.00003252735,0.00001342711,0.00004344313,0.0000710084,0.00003198116],"domain_scores_gemma":[0.9995901,0.0001458118,0.0001055915,0.0000432302,0.00008553942,0.0000297522],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003039874,0.0001744095,0.8717598,0.00006038433,0.0001305389,0.0001340459,0.000469312,0.01692216,0.04490151,0.0004235205,0.0003893439,0.06433101],"study_design_scores_gemma":[0.000003376455,0.00003522097,0.9758316,0.000006107945,0.00002041248,0.00003739797,0.0002735391,0.02079838,0.002635028,0.00005752778,0.0002950189,0.000006578302],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9985266,0.00002703559,0.000689419,0.000007011076,6.637121e-7,0.000005158395,0.0002870239,0.00001277916,0.0004442074],"genre_scores_gemma":[0.9964223,0.00004692812,0.002344965,0.000005937559,0.000002491698,0.000007184159,0.0009852535,0.000003815132,0.0001810905],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02899083,"threshold_uncertainty_score":0.05764419,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08249007556635264,"score_gpt":0.2707620266839153,"score_spread":0.1882719511175627,"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."}}