Scientific Modelling and Research

10.20448/808 ◽  
2020 ◽  
Keyword(s):  
Scientific Modelling

scholarly journals How to make progress in projecting climate change impacts

2013 ◽  
Vol 70 (6) ◽  
pp. 1069-1074 ◽  
Author(s):  
William W. L. Cheung ◽  
Daniel Pauly ◽  
Jorge L. Sarmiento
Keyword(s):  
Climate Change ◽  
Climate Change Impacts ◽  
Marine Ecosystems ◽  
Specific Reference ◽  
Marine Science ◽  
Climate Change Effects ◽  
Scientific Modelling ◽  
Maximum Body ◽  
Abundant Data ◽  
Maximum Body Size

Abstract Cheung, W. W. L., Pauly, D., and Sarmiento, J. L. 2013. How to make progress in projecting climate change impacts. – ICES Journal of Marine Science, 70: 1069–1074. Scientific modelling has become a crucial tool for assessing climate change impacts on marine resources. Brander et al. criticize the treatment of reliability and uncertainty of such models, with specific reference to Cheung et al. (2013, Nature Climate Change, 3: 254–258) and their projections of a decrease in maximum body size of marine fish under climate change. Here, we use the specific criticisms of Brander et al. (2013, ICES Journal of Marine Science) on Cheung et al. (2013) as examples to discuss ways to make progress in scientific modelling in marine science. We address the technical criticisms by Brander et al., then their more general comments on uncertainty. The growth of fish is controlled and limited by oxygen, as documented in a vast body of peer-reviewed literature that elaborates on a robust theory based on abundant data. The results from Cheung et al. were obtained using published, reproducible and peer-reviewed methods, and the results agree with the empirical data; the key assumptions and uncertainties of the analysis were stated. These findings can serve as a step towards improving our understanding of climate change impacts on marine ecosystems. We suggest that, as in other fields of science, it is important to develop incrementally (or radically) new approaches and analyses that extend, and ultimately improve, our understanding and projections of climate change effects on marine ecosystems.

scholarly journals Correction to: Self-Evaluative Scientific Modelling in an Outreach Gene Technology Laboratory

2020 ◽  
Vol 29 (6) ◽  
pp. 740-742
Author(s):  
Tamara Roth ◽  
Franz-Josef Scharfenberg ◽  
Julia Mierdel ◽  
Franz X. Bogner
Keyword(s):  
Gene Technology ◽  
Scientific Modelling

The regime against money laundering: a call for scientific modelling

2018 ◽  
Vol 21 (4) ◽  
pp. 584-593 ◽  
Author(s):  
Emmanuel Sotande
Keyword(s):  
Money Laundering ◽  
Design Methodology ◽  
Developing Economies ◽  
International Standards ◽  
Dark Side ◽  
Financial Flows ◽  
Content Type ◽  
Illicit Financial Flows ◽  
Scientific Modelling ◽  
Combat Money Laundering

PurposeThe purpose of this paper is to examine the magnitude of the global problem of money laundering and the scholarly critics of money laundering concept. The paper further explores the scientific modelling to combat money laundering transactions.Design/methodology/approachThe research methodology adopted was qualitative analysis. This was applied through the use and analysis of documents and expert interviews.FindingsThe paper reveals how the global displacement on the fight against money laundering is being determined by “attractiveness index”. This attractiveness index is the dark side affecting anti-money laundering (AML) concept within developing economies. The critics of the AML accounts for major discrepancies associated with the context of the term AML regimes and international standards to combat illicit financial flows.Social implicationsThe regimes against money laundering compel countries to adopt the same recommendations and standards and were not given opportunity to proffer their own creative alternatives within their own circumstances.Originality/valueThe paper suggests AML Transaction Validation Model in the quest to combat illicit financial flows originated from organized and serious crime within the global jurisdictions.

scholarly journals Simflowny 2: An upgraded platform for scientific modelling and simulation

2018 ◽  
Vol 229 ◽  
pp. 170-181 ◽  
Author(s):  
A. Arbona ◽  
B. Miñano ◽  
A. Rigo ◽  
C. Bona ◽  
C. Palenzuela ◽  
...  
Keyword(s):  
Modelling And Simulation ◽  
Scientific Modelling
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