{"id":"W1990506946","doi":"10.5589/m13-021","title":"Estimating grassland chlorophyll content using remote sensing data at leaf, canopy, and landscape scales","year":2013,"lang":"en","type":"article","venue":"Canadian Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"General Electric (Canada)","funders":"","keywords":"Canopy; Chlorophyll; Environmental science; Remote sensing; Vegetation (pathology); Chlorophyll a; Red edge; Leaf area index; Enhanced vegetation index; Geography; Normalized Difference Vegetation Index; Hyperspectral imaging; Agronomy; Botany; Biology; Vegetation Index","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0002535982,0.0002480221,0.0001705171,0.0009411178,0.0002390399,0.0003439708,0.0001820296,0.000159235,0.0003377729],"category_scores_gemma":[0.0006531952,0.0001456566,0.0002088707,0.0008119062,0.0001407422,0.0003224073,0.0002144279,0.0001045346,0.00009017396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008243557,"about_ca_system_score_gemma":0.0004958173,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1229203,"about_ca_topic_score_gemma":0.2720917,"domain_scores_codex":[0.999889,0.00001704246,0.000006763299,0.00003055491,0.0000382392,0.000018349],"domain_scores_gemma":[0.9998415,0.00003713343,0.00003764887,0.00001965011,0.00004823532,0.00001582941],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008450963,0.00006411617,0.7992701,0.0001033363,0.0001546135,0.0001537527,0.0003815766,0.01583975,0.07796058,0.0001605148,0.0002105189,0.1056167],"study_design_scores_gemma":[0.000004760743,0.00002303919,0.9773664,0.000007700575,0.00002629733,0.00003485352,0.0001546082,0.01936395,0.002630444,0.00006944948,0.0003086922,0.000009911138],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9945481,0.0000774006,0.004388538,0.00001095207,0.000001175366,0.00002110467,0.0002383875,0.00006140493,0.000652907],"genre_scores_gemma":[0.984774,0.00007244908,0.0144898,0.00001193632,0.000001772603,0.00001496371,0.0004696191,0.000008229446,0.0001573586],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1229203,"threshold_uncertainty_score":0.2444097,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03298827162770342,"score_gpt":0.2260956953536711,"score_spread":0.1931074237259677,"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."}}