{"id":"W4413463179","doi":"10.1016/j.rse.2025.114987","title":"A more precise retrieval of sun-induced chlorophyll fluorescence from satellite data using artificial neural networks","year":2025,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Water Quality Monitoring and Analysis","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"European Research Council; Office of Science; Horizon 2020 Framework Programme; National Natural Science Foundation of China; European Commission; U.S. Department of Energy","keywords":"Remote sensing; Satellite; Artificial neural network; Chlorophyll fluorescence; Fluorescence; Computer science; Environmental science; Chlorophyll a; Artificial intelligence; Geology; Physics; Optics; Astronomy; Botany; Biology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.000363266,0.0005436174,0.0003549576,0.0004239664,0.0001556731,0.0004168295,0.0002726145,0.0005567637,0.0003152328],"category_scores_gemma":[0.0007345827,0.0001977619,0.0004120926,0.0007102045,0.0001170836,0.0007642967,0.0002324558,0.0004727294,0.0001331427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004717103,"about_ca_system_score_gemma":0.0003457821,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01037983,"about_ca_topic_score_gemma":0.009487895,"domain_scores_codex":[0.9998745,0.00001771203,0.0000107888,0.00004886438,0.00003614273,0.00001192706],"domain_scores_gemma":[0.9998735,0.00003944524,0.00002558383,0.00001697535,0.00004013452,0.000004218381],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006552682,0.00008460035,0.007018457,0.00008293793,0.00007723885,0.00009517409,0.00004145839,0.8534086,0.04156522,0.0008959189,0.0005315359,0.09613329],"study_design_scores_gemma":[0.000002964973,0.00000612754,0.001991023,0.000003870253,0.000004953835,0.000004812686,0.00000478446,0.9947049,0.002842947,0.0002424757,0.0001840743,0.000006997097],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5461045,0.001013572,0.448355,0.0002930641,0.0000971343,0.00003093681,0.0005635347,0.0009021819,0.002640091],"genre_scores_gemma":[0.9132878,0.0003007841,0.08493806,0.00006296558,0.00002991545,0.00002675184,0.0005263838,0.00002572633,0.0008016577],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01037983,"threshold_uncertainty_score":0.02063882,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05386053647804312,"score_gpt":0.2859945825167152,"score_spread":0.2321340460386721,"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."}}