{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Albanito F"],"funding":["Integrative Research Group of the Global Research Alliance on agricultural GHGs","FACCE-JPI projects","COMET-Global","CN-MIP","Models4Pastures","MACSUR","Natural Environment Research Council","MAGGNET"],"pagination":["13485-13498"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9494747"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["56(18)"],"pubmed_abstract":["There is a growing realization that the complexity of model ensemble studies depends not only on the models used but also on the experience and approach used by modelers to calibrate and validate results, which remain a source of uncertainty. Here, we applied a multi-criteria decision-making method to investigate the rationale applied by modelers in a model ensemble study where 12 process-based different biogeochemical model types were compared across five successive calibration stages. The modelers shared a common level of agreement about the importance of the variables used to initialize their models for calibration. However, we found inconsistency among modelers when judging the importance of input variables across different calibration stages. The level of subjective weighting attribut"],"journal":["Environmental science & technology"],"pubmed_title":["How Modelers Model: the Overlooked Social and Human Dimensions in Model Intercomparison Studies."],"pmcid":["PMC9494747"],"funding_grant_id":["NE/V003259/1"],"pubmed_authors":["Bellocchi G","Albanito F","Ehrhardt F","Dorich C","Ludemann C","Doltra J","Doro L","Meyer R","Sandor R","Meier E","Harrison M","Brilli L","De Antoni Migliorati M","Martin R","Carozzi M","Grace P","McBey D","Recous S","Christie K","Smith P","Liebig M","Myrgiotis V","Grant B","Soussana JF","Leonard J","Fitton N","Smith WN","Bhatia A","Snow V"],"additional_accession":[]},"is_claimable":false,"name":"How Modelers Model: the Overlooked Social and Human Dimensions in Model Intercomparison Studies.","description":"There is a growing realization that the complexity of model ensemble studies depends not only on the models used but also on the experience and approach used by modelers to calibrate and validate results, which remain a source of uncertainty. Here, we applied a multi-criteria decision-making method to investigate the rationale applied by modelers in a model ensemble study where 12 process-based different biogeochemical model types were compared across five successive calibration stages. The modelers shared a common level of agreement about the importance of the variables used to initialize their models for calibration. However, we found inconsistency among modelers when judging the importance of input variables across different calibration stages. The level of subjective weighting attribut","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Sep","modification":"2025-04-22T01:52:50.952Z","creation":"2025-04-05T20:06:48.489Z"},"accession":"S-EPMC9494747","cross_references":{"pubmed":["36052879"],"doi":["10.1021/acs.est.2c02023"]}}