{"id":"W3082590915","doi":"10.1016/j.isprsjprs.2020.08.003","title":"Estimating crop biomass using leaf area index derived from Landsat 8 and Sentinel-2 data","year":2020,"lang":"en","type":"article","venue":"ISPRS Journal of Photogrammetry and Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":137,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Agriculture and Agri-Food Canada","funders":"Agriculture and Agri-Food Canada; Canadian Space Agency","keywords":"Leaf area index; Mean squared error; Remote sensing; Environmental science; Red edge; Multispectral image; Biomass (ecology); Canopy; Coefficient of determination; Crop; Mathematics; Vegetation (pathology); Agronomy; Statistics; Geography; Hyperspectral imaging; Forestry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003534432,0.0002627067,0.0004156329,0.00006146236,0.0002598067,0.0002279794,0.0002391619,0.0001527912,0.00002089298],"category_scores_gemma":[0.000313096,0.0001971152,0.00006918542,0.0004108302,0.0002594595,0.0003631179,0.0005179819,0.0004670786,0.000002950619],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000046689,"about_ca_system_score_gemma":0.00001662983,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002353859,"about_ca_topic_score_gemma":0.0001200515,"domain_scores_codex":[0.998193,0.0001257428,0.0005079344,0.0004421964,0.0004096428,0.0003214357],"domain_scores_gemma":[0.9986989,0.0001387397,0.0005096886,0.0002654832,0.00003882128,0.000348333],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009094265,0.00001662556,0.01343409,0.0000345041,0.0001025349,0.0005032819,0.0009695587,0.001456918,0.781197,4.261092e-8,0.0003699509,0.2018246],"study_design_scores_gemma":[0.0007776246,0.00004697886,0.01174183,0.0002647687,0.0001382805,0.001494222,0.0004209982,0.9693348,0.01475546,0.00009683402,0.0006259072,0.0003022971],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8800804,0.000279974,0.118608,0.0005706836,0.0002377281,0.00008486287,0.000006696133,0.00002321075,0.0001084513],"genre_scores_gemma":[0.8186011,0.00003907795,0.1805478,0.0004562195,0.000323259,1.02059e-9,0.000008439225,0.00002104256,0.000003142834],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9678779,"threshold_uncertainty_score":0.8038124,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03654260219640426,"score_gpt":0.2578545796938386,"score_spread":0.2213119774974344,"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."}}