Evidence for sea spray effect on oxygen stable isotopes in bone phosphate — Approximation and correction using Gaussian Mixture Model clustering

Göhring, Andrea; Mauder, Markus; Kröger, Peer; Mayr, Christoph; Carnap-Bornheim, Claus von; Hilberg, Volker; Grupe, Gisela

doi:10.1016/j.scitotenv.2019.04.072

Cited by 6 publications

(5 citation statements)

References 63 publications

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“…The relatively low information content detected for δ 18 O carbonate values can probably be explained by a quite strong “sea spray” effect in this isotopic system as detected by Göhring et al This effect causes a distinct enrichment in 18 O in terrestrial mammals from Haithabu and Schleswig, thus leading to an overlap of herbivores, carnivores, and omnivores. Although this effect has also been verified for δ 13 C carbonate and δ 18 O phosphate values, it is much stronger in δ 18 O carbonate values . Consequently, the high entropy values for δ 18 O carbonate might be a site‐specific result and must be verified for other datasets.…”

Section: Discussionmentioning

confidence: 85%

“…This might, however, usually not be a necessary step when applying the feature ranking method, but serves as a validation tool for implementing the entropy‐based feature ranking described in this paper. Nevertheless, GMM cluster analysis was validated as a useful tool for the interpretation of multi‐isotope datasets, previously …”

Section: Discussionmentioning

confidence: 99%

“…We would, however, like to emphasize that a multi‐isotope approach is (usually) clearly recommended wherever possible. Recent studies demonstrated that the analysis and interpretation of multiple isotopic systems result in a gain of information compared with common bivariate analyse . Consequently, the exclusion of certain isotopic systems from analysis will result in a certain loss of information.…”

Section: Discussionmentioning

confidence: 99%

“…Nevertheless, GMM cluster analysis was validated as a useful tool for the interpretation of multi-isotope datasets, previously. [4][5][6]8,31,32 Finally, it is important to mention that our feature ranking method cannot be applied to studies where the underlying research question does not assume different groups (clusters) in the dataset. This is because the entropy would be lowest for a well-structured dataset with at least two clusters, but high for an unstructured dataset lacking any clusters.…”

Section: Applicability Of the Entropy-based Feature Ranking Methodsmentioning

confidence: 99%

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Which isotopes should we choose? Entropy‐based feature ranking enables evaluation of the information content of stable isotopes in archaeofaunal material

Göhring

Mauder

Kröger

et al. 2020

Rapid Comm Mass Spectrometry

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Rationale Methods for multi‐isotope analyses are gaining in importance in anthropological, archaeological, and ecological studies. However, when material is limited (i.e., archaeological remains), it is obligatory to decide a priori which isotopic system(s) could be omitted without losing information. Methods We introduce a method that enables feature ranking of isotopic systems on the basis of distance‐based entropy. The feature ranking method is evaluated using Gaussian Mixture Model (GMM) clustering as well as a cluster validation index (“trace index”). Results Combinations of features resulting in high entropy values are less important than those resulting in low entropy values structuring the dataset into more distinct clusters. Therefore, this method allows us to rank isotopic systems. The isotope ranking depends on the analyzed dataset, for example, consisting of terrestrial mammals or fish. The feature ranking results were verified by cluster analysis. Conclusions Entropy‐based feature ranking can be used to a priori select the isotopic systems that should be analyzed. Consequently, we strongly suggest that this method should be applied if only limited material is available.

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Section: Discussionmentioning

confidence: 85%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Applicability Of the Entropy-based Feature Ranking Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Which isotopes should we choose? Entropy‐based feature ranking enables evaluation of the information content of stable isotopes in archaeofaunal material

Göhring

Mauder

Kröger

et al. 2020

Rapid Comm Mass Spectrometry

Self Cite

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“…GMC [ 41 , 42 , 43 , 44 ] based on the Expectation-Maximization (EM) cluster analysis algorithm is used to optimize the number of GRNN prediction models. GMC is a linear combination of multiple Gaussian distribution functions, which can fit any type of distribution, theoretically [ 45 ].…”

Section: Prediction Model and Resultsmentioning

confidence: 99%

Predicting Mechanical Properties of Cold-Rolled Steel Strips Using Micro-Magnetic NDT Technologies

2022

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Multiple micro-magnetic non-destructive testing (NDT) technologies are suitable candidates for predicting the mechanical properties of cold-rolled steel strips. In this work, based on magnetic domain dynamics behavior and magnetization theory, the correlation between electromagnetic characteristics extracted by multiple micro-magnetic NDT technologies and the influence factors was investigated. It was found that temperature and tension can subsequently affect the electromagnetic parameters by altering the domain structure and domain walls’ motion properties. Pearson’s correlation coefficients were employed to reflect the dependence of micromagnetic characteristics on influencing factors. The lift-off was determined as the largest influence factor among influence factors. A pseudo-static detection was reached by polynomial fitting, which could eliminate the influence of lift-off on the detection results. The number of training models was optimized, and the detection accuracy was improved via the improved Generalized Regression Neural Network (GRNN) model, based on the Gaussian Mixture Clustering (GMC) algorithm.

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Application of Gaussian Mixture Model Clustering for the Resolution of Multi-isotope Fingerprints

Göhring

Grupe²,

Kröger³

2023

Interdisciplinary Contributions to Archaeology

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Evidence for sea spray effect on oxygen stable isotopes in bone phosphate — Approximation and correction using Gaussian Mixture Model clustering

Cited by 6 publications

References 63 publications

Which isotopes should we choose? Entropy‐based feature ranking enables evaluation of the information content of stable isotopes in archaeofaunal material

Which isotopes should we choose? Entropy‐based feature ranking enables evaluation of the information content of stable isotopes in archaeofaunal material

Predicting Mechanical Properties of Cold-Rolled Steel Strips Using Micro-Magnetic NDT Technologies

Application of Gaussian Mixture Model Clustering for the Resolution of Multi-isotope Fingerprints

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