{"id":"W4383876264","doi":"10.5194/essd-15-2879-2023","title":"GRiMeDB: the Global River Methane Database of concentrations and fluxes","year":2023,"lang":"en","type":"article","venue":"Earth system science data","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Winnipeg","funders":"Norges Forskningsråd; Vetenskapsrådet; Svenska Forskningsrådet Formas; Umeå Universitet; National Science Foundation","keywords":"Environmental science; Fluvial; Methane; Ecosystem; STREAMS; Atmosphere (unit); Hydrology (agriculture); Flux (metallurgy); River ecosystem; Aquatic ecosystem; Atmospheric sciences; Database; Environmental chemistry; Ecology; Geology; Meteorology; Chemistry; Computer science; Geography","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":[0.001137579,0.001767216,0.001636352,0.005740264,0.0004088848,0.001740072,0.002258231,0.001062598,0.01613171],"category_scores_gemma":[0.003838393,0.0007355904,0.0007547727,0.01098427,0.0002177914,0.001935397,0.001966844,0.001233656,0.02131307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007817068,"about_ca_system_score_gemma":0.002028197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0298122,"about_ca_topic_score_gemma":0.02816873,"domain_scores_codex":[0.999005,0.0001310292,0.0002005632,0.0002352167,0.0003189592,0.0001093031],"domain_scores_gemma":[0.9981574,0.0002495589,0.0005372818,0.0003269297,0.0005421016,0.0001867074],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003510552,0.00005077165,0.0245039,0.003112751,0.0003689768,0.0001896505,0.0001439549,0.002841808,0.001430022,0.002943992,0.9322197,0.03184345],"study_design_scores_gemma":[0.0001809477,0.0000342805,0.03949526,0.0006168899,0.00009563315,0.00009869438,0.0001066928,0.003121639,0.001368612,0.002472502,0.9523081,0.0001007693],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0008663161,0.0003027696,0.0004833608,0.00005322387,0.00001880934,0.00001451721,0.9959806,0.001127362,0.001152951],"genre_scores_gemma":[0.003436658,0.0001849795,0.001417028,0.00003906013,0.00001166195,0.00006088235,0.9943111,0.0001982849,0.0003402582],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0298122,"threshold_uncertainty_score":0.0592773,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02223658453185895,"score_gpt":0.2550462789772868,"score_spread":0.2328096944454278,"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."}}