{"id":"W2073945694","doi":"10.1117/1.jrs.7.073567","title":"Leaf area index estimation in semiarid mixed grassland by considering both temporal and spatial variations","year":2013,"lang":"en","type":"article","venue":"Journal of Applied Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Leaf area index; Normalized Difference Vegetation Index; Grassland; Enhanced vegetation index; Environmental science; Remote sensing; Vegetation (pathology); Spatial variability; Atmospheric sciences; Mathematics; Vegetation Index; Geography; Statistics; Geology; Ecology","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":[0.0003990523,0.00021382,0.0001989533,0.001212054,0.0001208266,0.0002597472,0.0001665769,0.0001789261,0.0001464976],"category_scores_gemma":[0.0005085769,0.000144804,0.0001873892,0.0006934333,0.0000774755,0.0002972219,0.0001259294,0.0000733225,0.00004761889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001316972,"about_ca_system_score_gemma":0.0001234791,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003901149,"about_ca_topic_score_gemma":0.007092035,"domain_scores_codex":[0.9999206,0.0000183938,0.00000667712,0.00002606442,0.00001676201,0.00001142106],"domain_scores_gemma":[0.9998608,0.00004519507,0.00003734001,0.00001331911,0.00002950305,0.00001387354],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004717249,0.000226758,0.5385951,0.0001615091,0.0002544697,0.0007858349,0.0002960679,0.0791502,0.2003223,0.0003216084,0.0003471286,0.1790673],"study_design_scores_gemma":[0.0000107133,0.00007994945,0.5820437,0.0000061627,0.00007355833,0.0002330163,0.0001084199,0.4103805,0.006572958,0.0002081067,0.0002565646,0.00002625839],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9910459,0.0001220673,0.008560173,0.000007424921,0.000002227296,0.000002945031,0.00004466503,0.00003524857,0.0001793679],"genre_scores_gemma":[0.9944864,0.00004285108,0.005301726,0.000003877736,0.000003090908,0.000004268737,0.00008286847,0.000003585968,0.00007134824],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003901149,"threshold_uncertainty_score":0.007756889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005962597845940886,"score_gpt":0.1940692212303887,"score_spread":0.1881066233844478,"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."}}