• Journal of The Korean Astronomical Society
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• v.55 no.2
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• pp.23-36
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• 2022

We present a method to determine nitrogen abundance ratios with respect to iron ([N/Fe]) from molecular CN-band features observed in low-resolution (R ~ 2000) stellar spectra obtained by the Sloan Digital Sky Survey (SDSS) and the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST). Various tests are carried out to check the systematic and random errors of our technique, and the impact of signal-to-noise (S/N) ratios of stellar spectra on the determined [N/Fe]. We find that the uncertainty of our derived [N/Fe] is less than 0.3 dex for S/N ratios larger than 10 in the ranges T_eff = [4000, 6000] K, log g = [0.0, 3.5], [Fe/H] = [-3.0, 0.0], [C/Fe] = [-1.0, +4.5], and [N/Fe] = [-1.0, +4.5], the parameter space that we are interested in to identify N-enhanced stars in the Galactic halo. A star-by-star comparison with a sample of stars with [N/Fe] estimates available from the Apache Point Observatory Galactic Evolution Experiment (APOGEE) also suggests a similar level of uncertainty in our measured [N/Fe], after removing its systematic error. Based on these results, we conclude that our method is able to reproduce [N/Fe] from low-resolution spectroscopic data, with an uncertainty sufficiently small to discover N-rich stars that presumably originated from disrupted Galactic globular clusters.

• Korean Journal of Ecology and Environment
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• v.56 no.2
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• pp.161-171
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• 2023

The medium-large cladoceran species Simocephalus spp. predominantly occur in habitats with developed aquatic vegetation. Accordingly, due to Simocephalus' high contribution to zooplankton community biomass in the lake's littoral zone and wetland habitats, estimating their biomass is important to understand the matter cycling based on biological interactions within the aquatic food web. In this study, we reviewed the length-weight regression equations used previously to estimate Simocephalus biomass, directly measured S. serrulatus' body specification (length, width and area) and their biomass(dry weight) using instruments such as a microscopic digital camera and a microscale, and performed regression analysis between each other. When S. serrulatus biomass was estimated using the equation (Kawabata and Urabe, 1998) presented in 『Biomonitoring Survey and Assessment Manual』, Korea, errors between estimates and measures were relatively large compared to the S. serrulatus species-specific biomass estimate equation developed by Lemke and Benke (2003). In addition, both equations showed not only increasing trends in error (estimate-measure) with increasing S. serrulatus' body length, but also in error variance among similar-sized individuals. The results of regression analysis with dry weight by body specifications indicated that the most appropriate equation for estimating the biomass of S. serrulatus was derived from the width-dry weight exponential regression equation (R²=0.9555). The review and development study of such species-specific biomass estimation equations for zooplankton can be used as a tool to understand their role and function in aquatic ecosystem food webs.

• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.427-438
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• 2021

Two-stage sampling allows us to estimate population characteristics by both unit and cluster level together. Given a complex auxiliary information, integrated calibration weighting would better reflect the level-wise characteristics as well as multivariate characteristics between levels. This paper explored the integrated calibration weighting methods by Estevao and Särndal (2006) and Kim (2019) through a simulation study, where the efficiency of those weighting methods was compared using an artificial population data. Two weighting methods among others are shown efficient: single step calibration at the unit level with stacked individualized auxiliary information and iterative integrated calibration at each level. Under both methods, cluster calibrated weights are defined as the average of the calibrated weights of the unit(s) within cluster. Both were very good in terms of the goodness-of-fit of estimating the population totals of mutual auxiliary information between clusters and units, and showed small relative bias and relative mean square root errors for estimating the population totals of survey variables that are not included in calibration adjustments.