{"id":"W2156656621","doi":"10.1139/x05-043","title":"Multispectral remote sensing of landscape level foliar moisture: techniques and applications for forest ecosystem monitoring","year":2005,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Office of Experimental Program to Stimulate Competitive Research; National Aeronautics and Space Administration","keywords":"Environmental science; Remote sensing; Thematic Mapper; Principal component analysis; Multispectral image; Advanced Spaceborne Thermal Emission and Reflection Radiometer; Vegetation (pathology); Satellite; Radiometer; Radiometry; Water content; Atmospheric sciences; Satellite imagery; Geology; Digital elevation model; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.0004051588,0.0002653821,0.0001771426,0.001182091,0.0001941267,0.0003220147,0.0002677813,0.0002104014,0.002522732],"category_scores_gemma":[0.0004401302,0.0001535153,0.0001332733,0.00137117,0.0001526053,0.0003538086,0.0001761941,0.000221321,0.0004032043],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003596343,"about_ca_system_score_gemma":0.0003794173,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02072457,"about_ca_topic_score_gemma":0.07359885,"domain_scores_codex":[0.9998542,0.00002834741,0.000005701624,0.00001617755,0.00008505095,0.00001044017],"domain_scores_gemma":[0.9998722,0.0000272442,0.00001714018,0.00001633933,0.00005368932,0.00001338964],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00006212355,0.0001194609,0.02705566,0.0003497479,0.00005923084,0.00008005117,0.00007293596,0.004347578,0.06389991,0.001035818,0.006408765,0.8965086],"study_design_scores_gemma":[0.00009059079,0.0003342598,0.7957612,0.0001496072,0.0002222074,0.001156415,0.0002603689,0.08923689,0.0344983,0.006544607,0.07162579,0.0001196199],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4885563,0.04888982,0.3946054,0.0044215,0.0003092072,0.000700246,0.006294052,0.003565051,0.05265845],"genre_scores_gemma":[0.5573159,0.01600421,0.416268,0.0003893013,0.0001832728,0.0001709871,0.001505074,0.00007351989,0.008089653],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02072457,"threshold_uncertainty_score":0.04120785,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03436139973764322,"score_gpt":0.3033791435573101,"score_spread":0.2690177438196669,"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."}}