{"id":"W4297021392","doi":"10.1002/essoar.10512404.1","title":"Optimizing the isoprene emission model MEGAN with satellite and ground-based observational constraints","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Instituto Chico Mendes de Conservação da Biodiversidade; European Social Fund; Natural Environment Research Council; Natural Sciences and Engineering Research Council of Canada; Instituto Nacional de Pesquisas da Amazônia; Sight Research UK; Agencia Estatal de Investigación; National Aeronautics and Space Administration; California Institute of Technology; Jet Propulsion Laboratory","keywords":"Isoprene; Environmental science; Amazonian; Satellite; Atmospheric sciences; Remote sensing; Meteorology; Chemistry; Ecology; Geography; Physics; Amazon rainforest","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":[0.0008430764,0.0007371085,0.0005681805,0.000274024,0.0003429147,0.0006092074,0.0006703867,0.0007369488,0.00072302],"category_scores_gemma":[0.001716458,0.000491709,0.0007287908,0.0003425389,0.0004248645,0.000793399,0.0006598809,0.00101444,0.0001309589],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00105925,"about_ca_system_score_gemma":0.001426111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02718754,"about_ca_topic_score_gemma":0.02192779,"domain_scores_codex":[0.9998023,0.00005650083,0.00001005702,0.00006250507,0.00003760193,0.00003110972],"domain_scores_gemma":[0.9994729,0.0003033579,0.00006662217,0.00003912536,0.0000863292,0.00003165418],"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":[0.00001616203,0.00001062248,0.0008088492,0.000009233656,0.00001061546,0.000007204406,0.000005602098,0.9964413,0.0006708605,0.0003453384,0.00007421945,0.001600014],"study_design_scores_gemma":[0.000007157365,0.000007228528,0.000395619,0.000001467295,0.000004404125,0.000001912086,0.000002868766,0.9988322,0.000359687,0.000276305,0.0001078034,0.000003439756],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7778953,0.000389828,0.2128179,0.0003491161,0.00004774071,0.00006693646,0.001280873,0.0006734246,0.006478896],"genre_scores_gemma":[0.9760543,0.00005940949,0.02236247,0.00005582974,0.000009851142,0.00005534584,0.0007466835,0.00007483709,0.0005812746],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02718754,"threshold_uncertainty_score":0.05405855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02562710776197099,"score_gpt":0.2252490669604457,"score_spread":0.1996219591984747,"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."}}