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11 results for “base saturation”
Soil information on a regional scale: Two machine learning based approaches for predicting saturated hydraulic conductivity
<p><strong>Version 1.0 - This version is the final revised one.</strong></p> <p>This is the dataset accompanying the paper: Zeitfogel et al., Soil information on a regional scale: Two machine learning based approaches for predicting saturated hydraulic conductivity, published at Geoderma, 2023 (https://doi.org/10.1016/j.geoderma.2023.116418).</p> <p>Soil property and Ksat maps for Austria. The digital soil maps were generated based on a Machine Learning and PTF-based approach (indirect approach) and a pure Machine Learning based approach (direct approach). By downloading the datasets, you agree that we nor the provider of the used source datasets cannot be liable for the data provided.</p> <p>This study was funded by the Austrian Federal Ministry of Agriculture, Regions and Tourism (Project InfCapAT), the Austrian Academy of Science (Project RechAUT) and the Austrian Science Fund project P 31213.</p> <p> </p>
Global soil saturated hydraulic conductivity map using random forest in a Covariate-based GeoTransfer Functions (CoGTF) framework at 1 km resolution
<p>The global Ksat map at 1 km resolution was developed by harnessing the technological advances in machine learning and availability of remotely sensed surrogate information such as terrain, climate, vegetation, and soil covariates. We merge concepts of predictive soil mapping with a large data set of Ksat measurements and local information (soil, vegetation, climate) into covariate-based “Geo Transfer Functions'' (CoGTFs) to generate global estimates of Ksat values (to highlight the impact of Geo-referenced covariates including various remote sensing maps, we use the term Geotransfer function GTF and not pedotransfer function PTF; in the latter case, typically only soil properties are used to estimate Ksat).</p> <p>The Ksat dataset is provided in GeoTIFF format. A total of 4 files that represent different soil depths (0, 30, 60, and 100 cm) are provided. The Ksat values are log-transformed (log10 Ksat) and cm/day was selected as a standardized unit.</p> <p>The Global Ksat training dataset used for this study is available here:<br> <a href="https://doi.org/10.5281/zenodo.3752721">https://doi.org/10.5281/zenodo.3752721</a></p> <p>The R code used for this study is available here:<br> <a href="https://github.com/ETHZ-repositories/Ksat_mapping_2020">https://github.com/ETHZ-repositories/Ksat_mapping_2020</a></p> <p>For more details / to cite this dataset please use:</p> <ul> <li>Gupta, S., Lehmann, P., Bonetti, S., Papritz, A., and Or, D., (2020): <strong>Global prediction of soil saturated hydraulic conductivity using random forest in a Covariate-based Geo Transfer Functions (CoGTF) framework</strong>. Journal of Advances in Modeling Earth Systems,<strong> </strong>13(4), e2020MS002242. https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020MS002242</li> </ul> <p>Other datasets related to this project:</p> <p>The Global vG training dataset is available here:</p> <p><a href="https://doi.org/10.5281/zenodo.5547338">10.5281/zenodo.5547338</a></p> <p>Examples of using this dataset to generate van Genuchten parameters maps can be found in <a href="https://doi.org/10.5281/zenodo.6343570">10.5281/zenodo.6343570</a>.</p> <p>The study was supported by ETH Zurich (Grant ETH-18 18-1). We would like to thank Zhongwang Wei, Samuel Bickel and Simone Fatichi (ETH Zurich) for insightful discussions.</p> <p> </p> <p> </p>
Shelled Pteropod individual-based model output for the publication: The impact of aragonite saturation variability on shelled pteropods: An attribution study in the California current system
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Experimental Data for "Seismic wave attenuation and dispersion due to partial fluid saturation: Direct measurements and numerical simulations based on X-Ray CT"
<p>Experimental Data from a Berea sandstone sample. Includes X-ray CT scans and mechanical response of sample.</p> <p>Abstract</p> <p>Quantitatively assessing seismic attenuation caused by fluid pressure diffusion (FPD) in partially saturated rocks is challenging because of its sensitivity to the spatial fluid distribution. To address this challenge we performed depressurisation experiments to induce the exsolution of carbon dioxide from water in a Berea sandstone sample. In a first set of experiments we used medical X-ray computed tomography (CT) to characterise the fluid distribution. At an equilibrium pressure of ~1 MPa and applying a fluid pressure decline rate of ~0.6 MPa per minute, we allowed a change in saturation of less than 1 %. The gas was heterogeneously distributed along the length of the sample, with most of the gas exsolving near the sample outlet. In a second set of experiments, at the same pressure and temperature, following a very similar exsolution protocol, we measured the frequency dependent attenuation and modulus dispersion between 0.1 and 1000 Hz using the forced oscillation method. We observed significant attenuation and dispersion in the extensional and bulk deformation modes, however not in the shear mode. Lastly, we use the fluid distribution derived from the X-ray CT as an input for numerical simulations of FPD to compute the attenuation and modulus dispersion. The numerical solutions are in close agreement with the attenuation and modulus dispersion measured in the laboratory. Our methodology allows for accurately relating attenuation and dispersion to the fluid distribution, which can be applied to improving the seismic monitoring of the subsurface.</p>
Improving Phylogenies Based on Average Nucleotide Identity, Incorporating Saturation Correction and Non-Parametric Bootstrap Support
<p>Whole genome comparisons based on Average Nucleotide Identities (ANI) and the Genome-to-genome distance calculator have risen to prominence in rapidly classifying prokaryotic taxa using whole genome sequences. Some implementations have even been proposed as a new standard in species classification and have become a common technique for papers describing newly sequenced genomes. However, attempts to apply whole genome divergence data to delineation of higher taxonomic units and to phylogenetic inference have had difficulty matching those produced by more complex phylogenetic methods. We present a novel method for generating statistically supported phylogenies of archaeal and bacterial groups using a combined ANI and alignment fraction-based metric. For the test cases to which we applied the developed approach we obtained results comparable with other methodologies up to at least the family-level. The developed method uses non-parametric bootstrapping to gauge support for inferred groups. This method offers the opportunity to make use of whole-genome comparison data, that are already being generated, to quickly produce phylogenies including support for inferred groups. Additionally, the developed ANI methodology can assist classification of higher taxonomic groups.<br> <br> Included herein are supplemental materials, and all whole genome datasets used throughout the construction of this work.</p>
Nitrate-based Nutritional Formula for Oxygen Saturation and Patient-reported Outcomes in Covid-19
ClinicalTrials.gov study NCT05290298. IPD Sharing: NO. Countries: 1. Publications: 1.
Erythrocyte Transfusion Based on the Measurement of Central Venous Oxygen Saturation in Postoperative Cardiac Surgery
ClinicalTrials.gov study NCT02963883. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Improving Phylogenies Based on Average Nucleotide Identity, Incorporating Saturation Correction and Non-Parametric Bootstrap Support
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Data from: Phylotranscriptomics: saturated third codon positions radically influence the estimation of trees based on next-gen data
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PH Weighted Chemical Exchange Saturation Transfer MRI-Based Surgical Resection to Improve Survival in Patients With Glioblastoma
ClinicalTrials.gov study NCT06448286. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Saturation genome editing-based functional evaluation and clinical 2 classification of BRCA2 single nucleotide variants
GEO Series GSE270424. Homo sapiens. 144 samples. Type: Other.
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