2007
DOI: 10.1016/j.sedgeo.2007.03.007
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Genetically meaningful decomposition of grain-size distributions

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Cited by 257 publications
(169 citation statements)
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References 23 publications
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“…Furthermore the best-possible parametrisation of an end-member model is given with an optimal weight quantile (l opt ), which resulted in the highest explained variance compared to all other l and an optimal number of EM (q opt ) at the inflection point within the Q-R 2 mean plot of the l opt model in consensus with previous works (Weltje and Prins, 2007). Table 3.…”
Section: End-member Modelling Analysis (Emma)supporting
confidence: 59%
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“…Furthermore the best-possible parametrisation of an end-member model is given with an optimal weight quantile (l opt ), which resulted in the highest explained variance compared to all other l and an optimal number of EM (q opt ) at the inflection point within the Q-R 2 mean plot of the l opt model in consensus with previous works (Weltje and Prins, 2007). Table 3.…”
Section: End-member Modelling Analysis (Emma)supporting
confidence: 59%
“…Adding the set of modern lake surface samples to the Donggi Cona data Example how boundary parameters were defined for all similarly-likely model runs using the Nam Co core grain-size data set. (a) Q−R 2 mean plot after Dietze et al (2012) for selected weight quantiles (l) and numbers of end members (q) (q opt at the inflection point after Weltje and Prins (2007); q max at the first local maximum); (b) a 3-D pattern of the Q − R 2 mean plot including an example sequence of 10 l between 0 ( * after Miesch, 1976, corresponds here to l opt ) and the numerical maximum of l and the first 20 q that resulted in different end-member models, for which (c) shows the number of overlapping modes (only zero overlaps were taken into account for robust similarly-likely model runs).…”
Section: Robust End-member Loadingsmentioning
confidence: 99%
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“…The grain-size 321 distribution of the samples was measured during 90 seconds and the arithmetic mean was 322 calculated from the 92 size classes. Downcore grain-size distributions were unmixed using 323 the end-member modeling algorithm of Weltje and Prins (2007). 324…”
Section: Biogenic Opal 301mentioning
confidence: 99%
“…The supplementary material contains the grain-size data set and the R-script used for EMMA. EMMA introduces artificial modes, usually in classes where other EM have their primary modes or where other end-members overlap Weltje and Prins (2007); Dietze et al (2012a). This is due to the constraint that the model aims to produce extreme EM in order to describe most of the variability of the data set.…”
Section: Field Workmentioning
confidence: 99%