{"id":"W2164008183","doi":"10.1117/1.jrs.7.073599","title":"Comparative analysis of SPOT, Landsat, MODIS, and AVHRR normalized difference vegetation index data on the estimation of leaf area index in a mixed grassland ecosystem","year":2013,"lang":"en","type":"article","venue":"Journal of Applied Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Normalized Difference Vegetation Index; Remote sensing; Advanced very-high-resolution radiometer; Environmental science; Moderate-resolution imaging spectroradiometer; Leaf area index; Grassland; Vegetation (pathology); Image resolution; Enhanced vegetation index; Spectroradiometer; Arid; Multispectral pattern recognition; Satellite; Multispectral image; Geography; Vegetation Index; Geology; Reflectivity; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.00161688,0.0002863101,0.0002302712,0.002068207,0.0001728711,0.0005069173,0.0002092197,0.0002991742,0.0002764513],"category_scores_gemma":[0.001893511,0.0001370122,0.0002992127,0.001089138,0.0001537901,0.0005595215,0.0001975883,0.00008581879,0.0001032353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002689396,"about_ca_system_score_gemma":0.0002116532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004431844,"about_ca_topic_score_gemma":0.01006897,"domain_scores_codex":[0.999567,0.0001397643,0.00003130624,0.00008297154,0.0001451681,0.00003378523],"domain_scores_gemma":[0.998743,0.0006877413,0.0001219032,0.00007016707,0.0003038846,0.00007332158],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001717161,0.000366149,0.8345113,0.0002187528,0.0005362214,0.0004749637,0.0005239435,0.01116008,0.04209303,0.0003187512,0.0004501402,0.1076295],"study_design_scores_gemma":[0.00002125252,0.0002803727,0.9508184,0.00001356279,0.0001226432,0.0001559888,0.000345314,0.04257272,0.005158237,0.0001297972,0.0003582705,0.00002339967],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9982343,0.0001524647,0.001045782,0.00000814792,0.000003665854,0.000006566234,0.0001168377,0.00002048258,0.0004117382],"genre_scores_gemma":[0.9963373,0.00008407701,0.002978828,0.00000752784,0.000004166814,0.000006107234,0.0004501237,0.000006292703,0.0001254903],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004431844,"threshold_uncertainty_score":0.00881207,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02039910904644842,"score_gpt":0.2373313100413114,"score_spread":0.216932200994863,"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."}}