scholarly journals Prediction modeling of isoscapes for stable isotopes in China’s rice based on environmental similarity

2021 ◽  
Author(s):  
Meiling Sheng ◽  
Weixing Zhang ◽  
Jing Nie ◽  
Chunlin Li ◽  
A-Xing Zhu ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Stable Isotope ◽  
Geographical Origin ◽  
Mean Squared Error ◽  
Prediction Method ◽  
Rice Production ◽  
Data Sets ◽  
Rice Quality ◽  
Isotope Distribution ◽  
Environmental Similarity

Abstract Rice quality is directly related to human health, so it is important to have traceability systems that can trace inferior or contaminated rice back to its geographical origin. This ensures farming practices in substandard regions become better regulated to improve rice quality, origin labelling and consumer trust. However, tracing the origin of rice on the marketplace requires an accurate database benchmarking the isotope distribution over areas of rice production. Large stable isotope data sets can be used to determine the geographical origin of rice through predictive isoscape models. This study presents the first rice isoscape based on environmental similarity to predict the geospatial distribution of δ13C, δ2H and δ18O values of Chinese rice and provides uncertainty at every location such prediction is made. For this study, 794 rice samples were collected in 2017 from primary rice production regions of China. An independent verification shows that the predicted isotope distribution from this new approach is of high accuracy, with a root mean squared error (RMSE) of 0.51‰, 7.09‰ and 2.06‰ for δ13C, δ2H and δ18O values respectively. In addition, uncertainty in the spatial distribution of isotopes can be used to indicate the prediction accuracy and to guide future sampling. Our results indicate that an isoscape prediction method based on environmental similarity is effective to predict the spatial distribution of stable isotope in rice, and is an effective tool for building isotope distribution in rice over large areas with complex environment. This method could also be used to predict potential isotopic variations in future years due to climate change.

Modelling growing stock volume of forest stands with various ALS area-based approaches

2021 ◽  
Author(s):  
Karolina Parkitna ◽  
Grzegorz Krok ◽  
Stanisław Miścicki ◽  
Krzysztof Ukalski ◽  
Marek Lisańczuk ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Mean Squared Error ◽  
Prediction Method ◽  
Forest Stand ◽  
Boosted Regression Trees ◽  
Forest Stands ◽  
Individual Tree ◽  
Growing Stock ◽  
Modelling Techniques ◽  
Stock Volume

Abstract Airborne laser scanning (ALS) is one of the most innovative remote sensing tools with a recognized important utility for characterizing forest stands. Currently, the most common ALS-based method applied in the estimation of forest stand characteristics is the area-based approach (ABA). The aim of this study was to analyse how three ABA methods affect growing stock volume (GSV) estimates at the sample plot and forest stand levels. We examined (1) an ABA with point cloud metrics, (2) an ABA with canopy height model (CHM) metrics and (3) an ABA with aggregated individual tree CHM-based metrics. What is more, three different modelling techniques: multiple linear regression, boosted regression trees and random forest, were applied to all ABA methods, which yielded a total of nine combinations to report. An important element of this work is also the empirical verification of the methods for estimating the GSV error for individual forest stand. All nine combinations of the ABA methods and different modelling techniques yielded very similar predictions of GSV for both sample plots and forest stands. The root mean squared error (RMSE) of estimated GSV ranged from 75 to 85 m3 ha−1 (RMSE% = 20.5–23.4 per cent) and from 57 to 64 m3 ha−1 (RMSE% = 16.4–18.3 per cent) for plots and stands, respectively. As a result of the research, it can be concluded that GSV modelling with the use of different ALS processing approaches and statistical methods leads to very similar results. Therefore, the choice of a GSV prediction method may be more determined by the availability of data and competences than by the requirement to use a particular method.

scholarly journals Do galactic bars depend on environment?: an information theoretic analysis of Galaxy Zoo 2

2020 ◽  
Vol 501 (1) ◽  
pp. 994-1001
Author(s):  
Suman Sarkar ◽  
Biswajit Pandey ◽  
Snehasish Bhattacharjee
Keyword(s):  
Spatial Distribution ◽  
Mutual Information ◽  
Local Density ◽  
Statistical Significance ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Host Galaxy ◽  
Data Sets ◽  
Data Set ◽  
Information Theoretic

ABSTRACT We use an information theoretic framework to analyse data from the Galaxy Zoo 2 project and study if there are any statistically significant correlations between the presence of bars in spiral galaxies and their environment. We measure the mutual information between the barredness of galaxies and their environments in a volume limited sample (Mr ≤ −21) and compare it with the same in data sets where (i) the bar/unbar classifications are randomized and (ii) the spatial distribution of galaxies are shuffled on different length scales. We assess the statistical significance of the differences in the mutual information using a t-test and find that both randomization of morphological classifications and shuffling of spatial distribution do not alter the mutual information in a statistically significant way. The non-zero mutual information between the barredness and environment arises due to the finite and discrete nature of the data set that can be entirely explained by mock Poisson distributions. We also separately compare the cumulative distribution functions of the barred and unbarred galaxies as a function of their local density. Using a Kolmogorov–Smirnov test, we find that the null hypothesis cannot be rejected even at $75{{\ \rm per\ cent}}$ confidence level. Our analysis indicates that environments do not play a significant role in the formation of a bar, which is largely determined by the internal processes of the host galaxy.

scholarly journals Tracing the Geographical Origin of Rice Applied by Stable Isotope Analysis

2008 ◽  
Vol 55 (5) ◽  
pp. 250-252 ◽  
Author(s):  
Yaeko Suzuki ◽  
Rumiko Nakashita ◽  
Fumikazu Akamatsu ◽  
Takashi Korenaga
Keyword(s):  
Stable Isotope ◽  
Stable Isotope Analysis ◽  
Geographical Origin ◽  
Isotope Analysis

Characterization of the Geographical Origin of Pecorino Sardo Cheese by Casein Stable Isotope (13C/12C and15N/14N) Ratios and Free Amino Acid Ratios

2001 ◽  
Vol 49 (3) ◽  
pp. 1404-1409 ◽  
Author(s):  
Gavina Manca ◽  
Federica Camin ◽  
Gavina C. Coloru ◽  
Alessandra Del Caro ◽  
Daniela Depentori ◽  
...  
Keyword(s):  
Amino Acid ◽  
Stable Isotope ◽  
Free Amino Acid ◽  
Free Amino ◽  
Geographical Origin

scholarly journals The seven sisters DANCe

2018 ◽  
Vol 612 ◽  
pp. A70 ◽  
Author(s):  
J. Olivares ◽  
E. Moraux ◽  
L. M. Sarro ◽  
H. Bouy ◽  
A. Berihuete ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Model Comparison ◽  
Precise Determination ◽  
Model Parameters ◽  
Data Sets ◽  
Adequate Model ◽  
Comparison Results ◽  
Bayesian Evidence ◽  
Mass Segregation

Context. Membership analyses of the DANCe and Tycho + DANCe data sets provide the largest and least contaminated sample of Pleiades candidate members to date. Aims. We aim at reassessing the different proposals for the number surface density of the Pleiades in the light of the new and most complete list of candidate members, and inferring the parameters of the most adequate model. Methods. We compute the Bayesian evidence and Bayes Factors for variations of the classical radial models. These include elliptical symmetry, and luminosity segregation. As a by-product of the model comparison, we obtain posterior distributions for each set of model parameters. Results. We find that the model comparison results depend on the spatial extent of the region used for the analysis. For a circle of 11.5 parsecs around the cluster centre (the most homogeneous and complete region), we find no compelling reason to abandon King’s model, although the Generalised King model introduced here has slightly better fitting properties. Furthermore, we find strong evidence against radially symmetric models when compared to the elliptic extensions. Finally, we find that including mass segregation in the form of luminosity segregation in the J band is strongly supported in all our models. Conclusions. We have put the question of the projected spatial distribution of the Pleiades cluster on a solid probabilistic framework, and inferred its properties using the most exhaustive and least contaminated list of Pleiades candidate members available to date. Our results suggest however that this sample may still lack about 20% of the expected number of cluster members. Therefore, this study should be revised when the completeness and homogeneity of the data can be extended beyond the 11.5 parsecs limit. Such a study will allow for more precise determination of the Pleiades spatial distribution, its tidal radius, ellipticity, number of objects and total mass.

Recent Developments in Gradient Plasticity—Part II: Plastic Heterogeneity and Wavelets

2002 ◽  
Vol 124 (3) ◽  
pp. 358-364 ◽  
Author(s):  
Avraam A. Konstantinidis ◽  
Elias C. Aifantis
Keyword(s):  
Spatial Distribution ◽  
Wavelet Analysis ◽  
Gradient Theory ◽  
Discrete Wavelet ◽  
Experimental Measurements ◽  
Data Sets ◽  
Gradient Plasticity ◽  
Slip Step ◽  
Recent Developments ◽  
Heterogeneous Deformation

Wavelet analysis is used for describing heterogeneous deformation in different scales. Slip step height experimental measurements of monocrystalline alloy specimens subjected to compression are considered. The experimental data are subjected to discrete wavelet transform and the spatial distribution of deformation in different scales (resolutions) is calculated. At the finer scale the wavelet analyzed data are identical to the experimental measurements, while at the coarser scale the profile predicted by the wavelet analysis resembles the shear band solution profile provided by gradient theory in agreement with experimental observations. The different data sets provided by wavelet analysis are used to train a neural network in order to predict the spatial distribution of strain at resolutions higher than those possible by the available experimental probes. In addition, applications of wavelet analysis to interpret size effect data in torsion and bending at the micron scale are examined by deriving scale-dependent constitutive equations which are used for this purpose.

scholarly journals A new pan-European dataset for gridded daily average wind speed based on in-situ observations

2021 ◽  
Author(s):  
Jouke de Baar ◽  
Gerard van der Schrier ◽  
Irene Garcia-Marti ◽  
Else van den Besselaar
Keyword(s):  
Spatial Distribution ◽  
High Resolution ◽  
Cross Validation ◽  
Data Sets ◽  
Optimal Prediction ◽  
Auxiliary Variables ◽  
Climate Data ◽  
Ensemble Spread ◽  
Wind Fields

<p><strong>Objective</strong></p><p>The purpose of the European Copernicus Climate Change Service (C3S) is to support society by providing information about the past, present and future climate. For the service related to <em>in-situ</em> observations, one of the objectives is to provide high-resolution (0.1x0.1 and 0.25x0.25 degrees) gridded wind speed fields. The gridded wind fields are based on ECA&D daily average station observations for the period 1970-2020.</p><p><strong>Research question</strong> </p><p>We address the following research questions: [1] How efficiently can we provide the gridded wind fields as a statistically reliable ensemble, in order to represent the uncertainty of the gridding? [2] How efficiently can we exploit high-resolution geographical auxiliary variables (e.g. digital elevation model, terrain roughness) to augment the station data from a sparse network, in order to provide gridded wind fields with high-resolution local features?</p><p><strong>Approach</strong></p><p>In our analysis, we apply greedy forward selection linear regression (FSLR) to include the high-resolution effects of the auxiliary variables on monthly-mean data. These data provide a ‘background’ for the daily estimates. We apply cross-validation to avoid FSLR over-fitting and use full-cycle bootstrapping to create FSLR ensemble members. Then, we apply Gaussian process regression (GPR) to regress the daily anomalies. We consider the effect of the spatial distribution of station locations on the GPR gridding uncertainty.</p><p>The goal of this work is to produce several decades of daily gridded wind fields, hence, computational efficiency is of utmost importance. We alleviate the computational cost of the FSLR and GPR analyses by incorporating greedy algorithms and sparse matrix algebra in the analyses.</p><p><strong>Novelty</strong>   </p><p>The gridded wind fields are calculated as a statistical ensemble of realizations. In the present analysis, the ensemble spread is based on uncertainties arising from the auxiliary variables as well as from the spatial distribution of stations.</p><p>Cross-validation is used to tune the GPR hyper parameters. Where conventional GPR hyperparameter tuning aims at an optimal prediction of the gridded mean, instead, we tune the GPR hyperparameters for optimal prediction of the gridded ensemble spread.</p><p>Building on our experience with providing similar gridded climate data sets, this set of gridded wind fields is a novel addition to the E-OBS climate data sets.</p>

scholarly journals Increasing the Research Value of Digitized Fossil Museum Specimens via Integrated Stable Isotope Data

2018 ◽  
Vol 2 ◽  
Author(s):  
Sean Moran ◽  
Bruce MacFadden ◽  
Michelle Barboza
Keyword(s):  
Stable Isotope ◽  
Data Sets ◽  
Museum Specimens ◽  
Ancillary Data ◽  
Deep Time ◽  
Aggregated Data ◽  
Isotope Data ◽  
The Status ◽  
The Individual ◽  
Stable Isotope Data

Over the past several decades, thousands of stable isotope analyses (δ13C, δ18O) published in the peer-reviewed literature have advanced understanding of ecology and evolution of fossil mammals in Deep Time. These analyses typically have come from sampling vouchered museum specimens. However, the individual stable isotope data are typically disconnected from the vouchered specimens, and there likewise is no central repository for this information. This paper describes the status, potential, and value of the integration of stable isotope data in museum fossil collections. A pilot study in the Vertebrate Paleontology collection at the Florida Museum of Natural History has repatriated within Specify more than 1,000 legacy stable isotope data (mined from the literature) with the vouchered specimens by using ancillary non Darwin Core (DwC) data fields. As this database grows, we hope to both: validate previous studies that were done using smaller data sets; and ask new questions of the data that can only be addressed with larger, aggregated data sets. validate previous studies that were done using smaller data sets; and ask new questions of the data that can only be addressed with larger, aggregated data sets. Additionally, we envision that as the community gains a better understanding of the importance of these kinds of ancillary data to add value to vouchered museum specimens, then workflows, data fields, and protocols can be standardized.

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