{"status": "ok", "message-type": "work-list", "message-version": "1.0.0", "message": {"facets": {}, "total-results": 604756, "items": [{"indexed": {"date-parts": [[2026, 8, 18]], "date-time": "2026-08-18T09:34:05Z", "timestamp": 1787045645528, "version": "build-2736575974"}, "description": "Supplemental Information for 10.1021/acs.jcim.9b00975", "reference-count": 0, "publisher": "American Chemical Society (ACS)", "content-domain": {"domain": [], "crossmark-restriction": false}, "DOI": "10.1021/acs.jcim.9b00975.s001", "type": "component", "created": {"date-parts": [[2020, 4, 24]], "date-time": "2020-04-24T17:05:33Z", "timestamp": 1587747933000}, "source": "Crossref", "is-referenced-by-count": 0, "title": ["Evaluating Scalable Uncertainty Estimation Methods for Deep Learning-Based Molecular Property Prediction"], "prefix": "10.1021", "member": "316", "deposited": {"date-parts": [[2026, 8, 18]], "date-time": "2026-08-18T09:18:20Z", "timestamp": 1787044700000}, "score": 34.27882, "resource": {"primary": {"URL": "https://pubs.acs.org/doi/suppl/10.1021/acs.jcim.9b00975/suppl_file/ci9b00975_si_001.pdf"}}, "issued": {"date-parts": [[null]]}, "references-count": 0, "URL": "https://doi.org/10.1021/acs.jcim.9b00975.s001", "relation": {"is-component-of": [{"id-type": "doi", "id": "10.1021/acs.jcim.9b00975", "asserted-by": "object"}]}}, {"indexed": {"date-parts": [[2026, 10, 1]], "date-time": "2026-10-01T05:01:25Z", "timestamp": 1790830885771, "version": "4.1.0"}, "reference-count": 64, "publisher": "MDPI AG", "issue": "4", "license": [{"start": {"date-parts": [[2016, 3, 31]], "date-time": "2016-03-31T00:00:00Z", "timestamp": 1459382400000}, "content-version": "vor", "delay-in-days": 0, "URL": "https://creativecommons.org/licenses/by/4.0/"}], "content-domain": {"domain": [], "crossmark-restriction": false}, "short-container-title": ["Water"], "abstract": "<jats:p>Predictive uncertainty (PU) is defined as the probability of occurrence of an observed variable of interest, conditional on all available information. In this context, hydrological model predictions and forecasts are considered to be accessible but yet uncertain information. To estimate the PU of hydrological multi-model ensembles, we apply a method based on the use of copulas which enables modelling the dependency structures between variates independently of their marginal distributions. Given that the option to employ copula functions imposes certain limitations in the multivariate case, we model the multivariate distribution as a cascade of bivariate copulas by using the pair-copula construction. We apply a mixture of probability distributions to estimate the marginal densities and distributions of daily flow rates for various meteorological and hydrological situations. The proposed method is applied to a multi-model ensemble involving two hydrological and one statistical flow models at two gauge stations in the Moselle river basin. Verification and inter-comparison with other PU assessment methods show that copulas are well-suited for this scope and constitute a valid approach for predictive uncertainty estimation of hydrological multi-model predictions.</jats:p>", "DOI": "10.3390/w8040125", "type": "journal-article", "created": {"date-parts": [[2016, 3, 31]], "date-time": "2016-03-31T10:27:21Z", "timestamp": 1459420041000}, "page": "125", "update-policy": "https://doi.org/10.3390/mdpi_crossmark_policy", "source": "Crossref", "is-referenced-by-count": 25, "title": ["Predictive Uncertainty Estimation of Hydrological Multi-Model Ensembles Using Pair-Copula Construction"], "prefix": "10.3390", "volume": "8", "author": [{"given": "Bastian", "family": "Klein", "sequence": "first", "affiliation": [{"name": "Department M2—Water Balance, Forecasting and Predictions, Federal Institute of Hydrology (BfG), Am Mainzer Tor 1, Koblenz 56068, Germany"}], "role": [{"vocabulary": "crossref", "role": "author"}]}, {"given": "Dennis", "family": "Meissner", "sequence": "additional", "affiliation": [{"name": "Department M2—Water Balance, Forecasting and Predictions, Federal Institute of Hydrology (BfG), Am Mainzer Tor 1, Koblenz 56068, Germany"}], "role": [{"vocabulary": "crossref", "role": "author"}]}, {"given": "Hans-Ulrich", "family": "Kobialka", "sequence": "additional", "affiliation": [{"name": "Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS, Schloss Birlinghoven, Sankt Augustin 53757, Germany"}], "role": [{"vocabulary": "crossref", "role": "author"}]}, {"ORCID": "https://orcid.org/0000-0003-4417-7548", "authenticated-orcid": false, "given": "Paolo", "family": "Reggiani", "sequence": "additional", "affiliation": [{"name": "Department of Civil Engineering, University of Siegen, Paul-Bonatz-Str. 9-11, Siegen 57068, Germany"}], "role": [{"vocabulary": "crossref", "role": "author"}]}], "member": "1968", "published-online": {"date-parts": [[2016, 3, 31]]}, "reference": [{"key": "ref_1", "doi-asserted-by": "crossref", "first-page": "2739", "DOI": "10.1029/1999WR900099", "article-title": "Bayesian theory of probabilistic forecasting via deterministic hydrologic model", "volume": "35", "author": "Krzysztofowicz", "year": "1999", "journal-title": "Water Resour. 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