{"id":"W2147013722","doi":"10.1109/whispers.2009.5289085","title":"Progress in retrieving canopy structural parameters and chlorophyll content using the refined hyperspectral and multi-angle measurement concept and CASI data","year":2009,"lang":"en","type":"article","venue":"","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Hyperspectral imaging; Remote sensing; Nadir; Leaf area index; Canopy; Environmental science; Inversion (geology); Photochemical Reflectance Index; Spectral bands; Atmospheric correction; Reflectivity; Bidirectional reflectance distribution function; Computer science; Geology; Normalized Difference Vegetation Index; Geography; Optics; Satellite; Physics; Botany","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":[0.001485414,0.0008357466,0.0005910244,0.001193067,0.0003044824,0.001161253,0.001136357,0.0005273577,0.0006423545],"category_scores_gemma":[0.001384426,0.0003859874,0.0004627966,0.001441278,0.0006516305,0.002086577,0.0006096302,0.0006957448,0.0002401274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009519922,"about_ca_system_score_gemma":0.0009111601,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01751174,"about_ca_topic_score_gemma":0.0178934,"domain_scores_codex":[0.9995192,0.00008200739,0.00001826356,0.0001320925,0.0002232334,0.00002516828],"domain_scores_gemma":[0.9992207,0.0001686837,0.00007990577,0.0001972968,0.0003074245,0.00002596158],"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":[0.000371731,0.0004896866,0.06274687,0.0008618156,0.0003001402,0.0001305131,0.0005616767,0.1149814,0.3824689,0.007607672,0.001546091,0.4279335],"study_design_scores_gemma":[0.0001933191,0.000512985,0.1341434,0.0001330553,0.0003469925,0.0003777968,0.0004920971,0.6206892,0.2203393,0.002602592,0.01989455,0.0002748176],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.608721,0.00341525,0.3736377,0.000581649,0.00009188194,0.0002980318,0.001382487,0.001647437,0.0102246],"genre_scores_gemma":[0.5520846,0.00118352,0.443847,0.0001316687,0.00006618239,0.0001342573,0.001355838,0.0001133084,0.001083639],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01751174,"threshold_uncertainty_score":0.0348196,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1018929390296768,"score_gpt":0.2742141795127249,"score_spread":0.1723212404830481,"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."}}