{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T05:11:11Z","timestamp":1776143471593,"version":"3.50.1"},"reference-count":63,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T00:00:00Z","timestamp":1658102400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Critical Interface Network for Intensively Managed Landscapes (CINet)","award":["EAR #2012850"],"award-info":[{"award-number":["EAR #2012850"]}]},{"name":"Critical Interface Network for Intensively Managed Landscapes (CINet)","award":["#80NSSC21K0934"],"award-info":[{"award-number":["#80NSSC21K0934"]}]},{"name":"Critical Interface Network for Intensively Managed Landscapes (CINet)","award":["ECCS #2030285"],"award-info":[{"award-number":["ECCS #2030285"]}]},{"name":"Critical Interface Network for Intensively Managed Landscapes (CINet)","award":["CNS #2106692"],"award-info":[{"award-number":["CNS #2106692"]}]},{"name":"NASA New Investigator","award":["EAR #2012850"],"award-info":[{"award-number":["EAR #2012850"]}]},{"name":"NASA New Investigator","award":["#80NSSC21K0934"],"award-info":[{"award-number":["#80NSSC21K0934"]}]},{"name":"NASA New Investigator","award":["ECCS #2030285"],"award-info":[{"award-number":["ECCS #2030285"]}]},{"name":"NASA New Investigator","award":["CNS #2106692"],"award-info":[{"award-number":["CNS #2106692"]}]},{"name":"NSF","award":["EAR #2012850"],"award-info":[{"award-number":["EAR #2012850"]}]},{"name":"NSF","award":["#80NSSC21K0934"],"award-info":[{"award-number":["#80NSSC21K0934"]}]},{"name":"NSF","award":["ECCS #2030285"],"award-info":[{"award-number":["ECCS #2030285"]}]},{"name":"NSF","award":["CNS #2106692"],"award-info":[{"award-number":["CNS #2106692"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Ecohydrological models vary in their sensitivity to forcing data and use available information to different extents. We focus on the impact of forcing precision on ecohydrological model behavior particularly by quantizing, or binning, time-series forcing variables. We use rate-distortion theory to quantize time-series forcing variables to different precisions. We evaluate the effect of different combinations of quantized shortwave radiation, air temperature, vapor pressure deficit, and wind speed on simulated heat and carbon fluxes for a multi-layer canopy model, which is forced and validated with eddy covariance flux tower observation data. We find that the model is more sensitive to radiation than meteorological forcing input, but model responses also vary with seasonal conditions and different combinations of quantized inputs. While any level of quantization impacts carbon flux similarly, specific levels of quantization influence heat fluxes to different degrees. This study introduces a method to optimally simplify forcing time series, often without significantly decreasing model performance, and could be applied within a sensitivity analysis framework to better understand how models use available information.<\/jats:p>","DOI":"10.3390\/e24070994","type":"journal-article","created":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T11:32:23Z","timestamp":1658143943000},"page":"994","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Evaluating Ecohydrological Model Sensitivity to Input Variability with an Information-Theory-Based Approach"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8282-5185","authenticated-orcid":false,"given":"Mozhgan A.","family":"Farahani","sequence":"first","affiliation":[{"name":"Department of Civil Engineering, University of Colorado Denver, Denver, CO 80204, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5079-4617","authenticated-orcid":false,"given":"Alireza","family":"Vahid","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, University of Colorado Denver, Denver, CO 80204, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9351-557X","authenticated-orcid":false,"given":"Allison E.","family":"Goodwell","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, University of Colorado Denver, Denver, CO 80204, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1002\/2015WR017558","article-title":"A new framework for comprehensive, robust, and efficient global sensitivity analysis: 1. Theory","volume":"52","author":"Razavi","year":"2016","journal-title":"Water Resour. Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e2021WR029911","DOI":"10.1029\/2021WR029911","article-title":"Importance of Parameter and Climate Data Uncertainty for Future Changes in Boreal Hydrology","volume":"57","author":"Marshall","year":"2021","journal-title":"Water Resour. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/S0022-1694(01)00421-8","article-title":"Equifinality, data assimilation, and uncertainty estimation in mechanistic modeling of complex environmental systems using the GLUE methodology","volume":"249","author":"Beven","year":"2001","journal-title":"J. Hydrol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1273","DOI":"10.4319\/lo.1997.42.5_part_2.1273","article-title":"Models of harmful algal blooms","volume":"42","author":"Franks","year":"1997","journal-title":"Limnol. Oceanogr."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"751","DOI":"10.1029\/97WR03495","article-title":"Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information","volume":"34","author":"Gupta","year":"1998","journal-title":"Water Resour. Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2008WR006836","article-title":"Model complexity control for hydrologic prediction","volume":"44","author":"Schoups","year":"2008","journal-title":"Water Resour. Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2002WR001746","article-title":"Effective and efficient algorithm for multiobjective optimization of hydrologic models","volume":"39","author":"Vrugt","year":"2003","journal-title":"Water Resour. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.jhydrol.2004.03.040","article-title":"The distributed model intercomparison project (DMIP): Motivation and experiment design","volume":"298","author":"Smith","year":"2004","journal-title":"J. Hydrol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.envsoft.2019.01.012","article-title":"Why so many published sensitivity analyses are false: A systematic review of sensitivity analysis practices","volume":"114","author":"Saltelli","year":"2019","journal-title":"Environ. Model. Softw."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3070","DOI":"10.1002\/2014WR016527","article-title":"What do we mean by sensitivity analysis? The need for comprehensive characterization of \u201cglobal\u201d sensitivity in Earth and Environmental systems models","volume":"51","author":"Razavi","year":"2015","journal-title":"Water Resour. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"6534","DOI":"10.1029\/2018WR023692","article-title":"Information Theory for Model Diagnostics: Structural Error is Indicated by Trade-Off Between Functional and Predictive Performance","volume":"55","author":"Ruddell","year":"2019","journal-title":"Water Resour. Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1111\/0272-4332.00040","article-title":"Sensitivity Analysis for Importance Assessment","volume":"22","author":"Saltelli","year":"2002","journal-title":"Risk Anal."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1835","DOI":"10.1175\/JHM-D-17-0209.1","article-title":"Benchmarking and process diagnostics of land models","volume":"19","author":"Nearing","year":"2018","journal-title":"J. Hydrometeorol."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., and Tarantola, S. (2008). Global Sensitivity Analysis. The Primer, John Wiley & Sons. Chapter 6.","DOI":"10.1002\/9780470725184"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e14251","DOI":"10.1002\/hyp.14251","article-title":"Ecohydrological modelling in a deciduous boreal forest: Model evaluation for application in non-stationary climates","volume":"35","author":"Marshall","year":"2021","journal-title":"Hydrol. Process."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Legleiter, C., Kyriakidis, P., Mcdonald, R., and Nelson, J. (2011). Effects of uncertain topographic input data on two-dimensional flow modeling in a gravel-bed river. Water Resour. Res., 47.","DOI":"10.1029\/2010WR009618"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.envsoft.2013.04.006","article-title":"An approach for global sensitivity analysis of a complex environmental model to spatial inputs and parameters: A case study of an agro-hydrological model","volume":"47","author":"Moreau","year":"2013","journal-title":"Environ. Model. Softw."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Clark, M.P., Slater, A.G., Rupp, D.E., Woods, R.A., Vrugt, J.A., Gupta, H.V., Wagener, T., and Hay, L.E. (2008). Framework for Understanding Structural Errors (FUSE): A modular framework to diagnose differences between hydrological models. Water Resour. Res., 44.","DOI":"10.1029\/2007WR006735"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.envsoft.2013.09.022","article-title":"A General Probabilistic Framework for uncertainty and global sensitivity analysis of deterministic models: A hydrological case study","volume":"51","author":"Baroni","year":"2014","journal-title":"Environ. Model. Softw."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1016\/j.cageo.2005.10.007","article-title":"Variance-based sensitivity analysis of the probability of hydrologically induced slope instability","volume":"32","author":"Hamm","year":"2006","journal-title":"Comput. Geosci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"e2020WR027948","DOI":"10.1029\/2020WR027948","article-title":"Understanding the Information Content in the Hierarchy of Model Development Decisions: Learning From Data","volume":"57","author":"Gharari","year":"2021","journal-title":"Water Resour. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2779","DOI":"10.1029\/2018WR023403","article-title":"Uncertainties in Snowpack Simulations\u2014Assessing the Impact of Model Structure, Parameter Choice, and Forcing Data Error on Point-Scale Energy Balance Snow Model Performance","volume":"55","author":"Marke","year":"2019","journal-title":"Water Resour. Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2707","DOI":"10.1002\/2014WR016498","article-title":"Evaluating snow models with varying process representations for hydrological applications","volume":"51","author":"Magnusson","year":"2015","journal-title":"Water Resour. Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.advwatres.2012.07.013","article-title":"A comparison of 1701 snow models using observations from an alpine site","volume":"55","author":"Essery","year":"2013","journal-title":"Adv. Water Resour."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3153","DOI":"10.5194\/hess-19-3153-2015","article-title":"Exploring the impact of forcing error characteristics on physically based snow simulations within a global sensitivity analysis framework","volume":"19","author":"Raleigh","year":"2015","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2019WR026471","article-title":"Debates: Does Information Theory Provide a New Paradigm for Earth Science? Sharper Predictions Using Occam\u2019s Digital Razor","volume":"56","author":"Weijs","year":"2020","journal-title":"Water Resour. Res."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1002\/j.1538-7305.1948.tb00917.x","article-title":"A Mathematical Theory of Communication","volume":"27","author":"Shannon","year":"1948","journal-title":"Bell Syst. Tech. J."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Manshour, P., Balasis, G., Consolini, G., Papadimitriou, C., and Palu\u0161, M. (2021). Causality and Information Transfer Between the Solar Wind and the Magnetosphere\u2013Ionosphere System. Entropy, 23.","DOI":"10.3390\/e23040390"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"e2019WR026133","DOI":"10.1029\/2019WR026133","article-title":"Information Flows: Characterizing Precipitation-Streamflow Dependencies in the Colorado Headwaters With an Information Theory Approach","volume":"56","author":"Franzen","year":"2020","journal-title":"Water Resour. Res."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"5920","DOI":"10.1002\/2016WR020216","article-title":"Temporal Information Partitioning: Characterizing synergy, uniqueness, and redundancy in interacting environmental variables","volume":"53","author":"Goodwell","year":"2017","journal-title":"Water Resour. Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5899","DOI":"10.1002\/2016WR020218","article-title":"Temporal Information Partitioning Networks (TIPNets): A process network approach to infer ecohydrologic shifts","volume":"53","author":"Goodwell","year":"2017","journal-title":"Water Resour. Res."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1841","DOI":"10.1002\/2016WR019768","article-title":"Process connectivity in a naturally prograding river delta","volume":"53","author":"Sendrowski","year":"2017","journal-title":"Water Resour. Res."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"4844","DOI":"10.3390\/e15114844","article-title":"Statistical Mechanics and Information-Theoretic Perspectives on Complexity in the Earth System","volume":"15","author":"Balasis","year":"2013","journal-title":"Entropy"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2545","DOI":"10.5194\/hess-14-2545-2010","article-title":"Why hydrological predictions should be evaluated using information theory","volume":"14","author":"Weijs","year":"2010","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1175\/JHM-D-15-0063.1","article-title":"Benchmarking NLDAS-2 Soil Moisture and Evapotranspiration to Separate Uncertainty Contributions","volume":"17","author":"Nearing","year":"2016","journal-title":"J. Hydrometeorol."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Sendrowski, A., Sadid, K., Meselhe, E., Wagner, W., Mohrig, D., and Passalacqua, P. (2018). Transfer Entropy as a Tool for Hydrodynamic Model Validation. Entropy, 20.","DOI":"10.3390\/e20010058"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"e2019WR024908","DOI":"10.1029\/2019WR024908","article-title":"The utility of information flow in formulating discharge forecast models: A case study from an arid snow-dominated catchment","volume":"56","author":"Tennant","year":"2020","journal-title":"Water Resour. Res."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"5003","DOI":"10.1002\/2014WR015874","article-title":"Estimating information entropy for hydrological data: One-dimensional case","volume":"50","author":"Gong","year":"2014","journal-title":"Water Resour. Res."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Cover, T.M., and Thomas, J.A. (2005). Elements of Information Theory, John Wiley & Sons. [2nd ed.].","DOI":"10.1002\/047174882X"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Drewry, D.T., Kumar, P., Long, S., Bernacchi, C., Liang, X.Z., and Sivapalan, M. (2010). Ecohydrological responses of dense canopies to environmental variability: 1. Interplay between vertical structure and photosynthetic pathway. J. Geophys. Res. Biogeosci., 115.","DOI":"10.1029\/2010JG001340"},{"key":"ref_41","first-page":"1","article-title":"Ecohydrological responses of dense canopies to environmental variability: 2. Role of acclimation under elevated CO2","volume":"115","author":"Drewry","year":"2010","journal-title":"J. Geophys. Res. Biogeosci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1007\/978-94-017-0519-6_48","article-title":"A Model Predicting Stomatal Conductance and Its Contribution to the Control of Photosynthesis Under Different Environmental Conditions","volume":"4","author":"Ball","year":"1987","journal-title":"Prog. Photosynth. Res."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Leclerc, M.Y., and Foken, T. (2014). Footprints in Micrometeorology and Ecology, Spriner.","DOI":"10.1007\/978-3-642-54545-0"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1007\/BF00120530","article-title":"Footprint prediction of scalar fluxes from analytical solutions of the diffusion equation","volume":"50","author":"Schuepp","year":"1990","journal-title":"Bound.-Layer Meteorol."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Hernandez Rodriguez, L.C., Goodwell, A.E., and Kumar, P. (Water Resour. Res., 2021). Inside the flux footprint: Understanding the role of organized land cover heterogeneity on land-atmospheric fluxes, Water Resour. Res., in review.","DOI":"10.2139\/ssrn.4034618"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1121","DOI":"10.1175\/JAMC-D-14-0321.1","article-title":"Climatology, variability and trends in United States vapor pressure deficit, an important fire-related meteorological quantity","volume":"54","author":"Seager","year":"2015","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_47","unstructured":"Sayood, K. (2018). Introduction to Data Compression, Elsevier. [5th ed.]. The Morgan Kaufmann Series in Multimedia Information and Systems, Morgan Kaufmann."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1093\/biomet\/66.3.605","article-title":"On optimal and data-based histograms","volume":"66","author":"Scott","year":"1979","journal-title":"Biometrika"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1109\/TIT.1982.1056489","article-title":"Least squares quantization in PCM","volume":"28","author":"Lloyd","year":"1982","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2788","DOI":"10.1109\/TIT.2011.2181938","article-title":"Interference channels with rate-limited feedback","volume":"58","author":"Vahid","year":"2011","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2913","DOI":"10.1109\/TCOMM.2016.2574867","article-title":"Approximate capacity region of the MISO broadcast channels with delayed CSIT","volume":"64","author":"Vahid","year":"2016","journal-title":"IEEE Trans. Commun."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2462","DOI":"10.1109\/TCAD.2018.2857059","article-title":"Extending flash lifetime in embedded processors by expanding analog choice","volume":"37","author":"Mappouras","year":"2018","journal-title":"IEEE Trans. -Comput.-Aided Des. Integr. Circuits Syst."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Vahid, A. (2021, January 11\u201316). Distortion-Based Outer-Bounds for Channels with Rate-Limited Feedback. Proceedings of the IEEE International Symposium on Information Theory (ISIT), Melbourne, Australia.","DOI":"10.1109\/ISIT45174.2021.9518024"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Gupta, H.V., Ehsani, M.R., Roy, T., Sans-Fuentes, M.A., Ehret, U., and Behrangi, A. (2021). Computing Accurate Probabilistic Estimates of One-D Entropy from Equiprobable Random Samples. Entropy, 23.","DOI":"10.3390\/e23060740"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"e2019WR024940","DOI":"10.1029\/2019WR024940","article-title":"Debates\u2014Does Information Theory Provide a New Paradigm for Earth Science? Causality, Interaction, and Feedback","volume":"56","author":"Goodwell","year":"2020","journal-title":"Water Resour. Res."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2515","DOI":"10.1002\/2015WR017200","article-title":"A unified approach for process-based hydrologic modeling: 2. Model implementation and case studies","volume":"51","author":"Clark","year":"2015","journal-title":"Water Resour. Res."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"677","DOI":"10.5194\/gmd-4-677-2011","article-title":"The Joint UK Land Environment Simulator (JULES), model description\u2014Part 1: Energy and water fluxes","volume":"4","author":"Best","year":"2011","journal-title":"Geosci. Model Dev."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Niu, G.Y., Yang, Z.L., Mitchell, K.E., Chen, F., Ek, M.B., Barlage, M., Kumar, A., Manning, K., Niyogi, D., and Rosero, E. (2011). The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements. J. Geophys. Res. Atmos., 116.","DOI":"10.1029\/2010JD015139"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1016\/j.jhydrol.2015.02.013","article-title":"Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications","volume":"523","author":"Song","year":"2015","journal-title":"J. Hydrol."},{"key":"ref_60","first-page":"407","article-title":"Sensitivity analysis for non-linear mathematical models","volume":"1","year":"1993","journal-title":"Math. Model. Comput. Exp."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"McKay, M.D. (1995). Evaluating Prediction Uncertainty, Nuclear Regulatory Commission. Technical Report.","DOI":"10.2172\/29432"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.envsoft.2017.02.001","article-title":"Comparison of variance-based and moment-independent global sensitivity analysis approaches by application to the SWAT model","volume":"91","author":"Nossent","year":"2017","journal-title":"Environ. Model. Softw."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"150904104740009","DOI":"10.1175\/JHM-D-14-0235.1","article-title":"How Does Availability of Meteorological Forcing Data Impact Physically Based Snowpack Simulations?","volume":"17","author":"Raleigh","year":"2016","journal-title":"J. Hydrometeorol."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/24\/7\/994\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:53:08Z","timestamp":1760140388000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/24\/7\/994"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,18]]},"references-count":63,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["e24070994"],"URL":"https:\/\/doi.org\/10.3390\/e24070994","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,18]]}}}