PNNL-13501 - Pacific Northwest National Laboratory

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Application of a Crop Model The resolution of global climate models is far too coarse to drive crop models, particularly in regions with topography as complex as in the Pacific Northwest. The high spatial resolution meteorology simulated by the PNNL RCM provides the opportunity to drive crop models directly at the farm levels. The Erosion Productivity Impact Calculator was driven by meteorology simulated by the regional climate model to assess the impact of a 2xCO2 “greenhouse” scenario of climate change on winter wheat production in the Palouse region in eastern Washington and portions of Oregon and Idaho. The climate scenarios were extracted from the regional climate model baseline and 2xCO2 simulations for each 90 km grid cell of the regional climate model, with differentiation by elevation. Winter wheat crop yields for the baseline climate averaged 4.52 Mg ha -1 across the study region. The lowest yields (0 Mg ha -1 ) occurred at the higher elevations where temperatures were not sufficient to allow for crop maturity. The highest yields (7.32 Mg ha -1 ) occurred at intermediate elevations with sufficient precipitation and mild temperatures. Winter wheat mean yield increased to 5.45 Mg ha -1 for the 2xCO2 climate, which was markedly warmer and wetter than baseline. Yield increases were principally explained by decreases in both cold temperature and water stress. Development of a Problem-Solving Environment The collaboration center modeling system must be intuitive and convenient if it is to be useful to the climate impact assessment community. This will require integration and coordination of each of the steps involved in using the modeling system. A user interface for the modeling system has been designed to guide the user through the process of defining the regional domain and resolution, selecting and interpolating the boundary conditions, compiling and running the regional climate model, mapping the regional climate simulation to the selected resolution, and displaying the data. As a proof of concept, a graphical user interface is being developed to facilitate the preprocessing of information that provides all data necessary for performing regional climate simulations. The model preprocessing is a very straightforward procedure masked by repetitive steps of script editing, source code editing, and data file renaming. By wrapping this process with Java codes to do the nonintuitive and repetitive parts, the user will be given a friendly, simple interface to the model. 240 FY 2000 Laboratory Directed Research and Development Annual Report Summary and Conclusions Major accomplishments have been achieved toward the development of a prototype Collaboration Center. A twodimensional decomposition parallel version of the Laboratory’s regional climate model has been developed and tested on various supercomputing platforms. The hydrologic model has been enhanced to represent river basins of different spatial scales. A reservoir model and an optimization routine can be used to evaluate the impacts of climate change using realistic water management strategies for the altered climate conditions. The procedures of using regional climate model outputs to assess climate change impacts on crop yields have been demonstrated using regional climate model simulations of the control and 2xCO2 conditions to drive a crop model over the Pacific Northwest Palouse region. A prototype graphical user interface is being developed to facilitate the preprocessing of information for performing regional climate simulations. With the completion of these tasks, we will be ready to operate an Collaboration Center to deliver information of relevance to the climate change impact assessment community. References Leung LR and SJ Ghan. 1995. “A subgrid parameterization of orographic precipitation.” Theoretical and Applied Climatology 52:95-118. Leung LR and SJ Ghan. 1998. “Parameterizing subgrid orographic precipitation and surface cover in climate models.” Mon Wea Rev. 3271-3291. Leung LR, MS Wigmosta, SJ Ghan, DJ Epstein, and LW Vail. 1996. “Application of a subgrid orographic precipitation/surface hydrology scheme to a mountain watershed.” J. Geophys. Res. 101:12,803-12,817. Wigmosta MS, LW Vail, and DP Lettenmaier. 1994. “A distributed hydrology-vegetation model for complex terrain.” Water Resour. Res. 30:1665-1679. Publications Contributor 1999. “Impacts of climate variability and change in the Pacific Northwest.” A Regional Report for the USGCRP National Assessment. Pacific Northwest Regional Assessment Group, pp 109.
Contributor 2000. “Chapter 10. Regional Climate Information – Evaluation and Projections.” IPCC Third Scientific Assessment of Climate Change, Cambridge University Press (in review). Kothari SC, Y Deng, J Cho, S Mitra, X Bian, LR Leung, SJ Ghan, and AJ Bourgeois. 2000. “Mesoscale climate modeling on PC clusters.” In Proceedings of the Second International Conference on High Performance Computing, New Delhi, India. Kothari SC, Y Deng, J Cho, S Mitra, X Bian, LR Leung, SJ Ghan, and AJ Bourgeois. 2000. “Software toolkit for PC clusters with application to climate modeling.” Journal of Computational Science (in review). Mote P, A Hamlet, and LR Leung. 2000. “Chapter 4: Possible future climate.” In Rhythms of Change: An Integrated Assessment of Climate Impacts on the Pacific Northwest, MIT Press (in press). Thomson AM, RA Brown, SJ Ghan, RC Izaurralde, NJ Rosenberg, and LR Leung. 2000. “Potential impacts of climate change on winter wheat production in eastern Washington state: A simulation study linking the PNNL Regional Climate Model with EPIC.” Climatic Change (submitted). Wigmosta MS and LR Leung. 2000. “Potential climate change impacts on flooding and streamflow in forested basins.” In The Influence of Environmental Change on Geomorphological Hazards in Forested Areas, RC Sidle and M Chigira (eds.), Centre for Agriculture and Biosciences International (in review). Earth System Science 241
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Laboratory Directed Research and De
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Laboratory Directed Research and De
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Computational Science and Engineeri
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Policy and Management Sciences Asse
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Introduction Department of Energy O
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5. After reviews by the Technical a
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• The Technical Council peer revi
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methods applicable to combustion, s
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Summary Each national laboratory mu
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Study Control Number: PN0004/1411 A
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Table 1. Positive and negative ions
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m/z Arbitrary Units Figure 1. Mass
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introduce low-volatility compounds
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arrangement, but the charge is reta
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two liquid chromatographic gradient
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Integrated Microfabricated Devices
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Matrix Assisted Laser Desorption/Io
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Molecular Beam Synthesis and Charac
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temperature distribution of the des
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expected to result in significant a
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Study Control Number: PN99069/1397
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Seuss DT and KA Prather. 1999. “M
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Analysis of Receptor-Modulated Ion
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laboratory (Lu and Xie 1997; Lu et
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Automated DNA Fingerprinting Microa
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Comparison of 4 Xanthomonas Isolate
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Approach Hematopoietic (bone marrow
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Anti-Human lgG Human lgG Protein G
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Characterization of Global Gene Reg
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PCR products for immobilization on
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identify novel proteins of importan
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Immunoprecipitaton of tyrosinephosp
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Coupled NMR and Confocal Microscopy
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In the optical microscopy image, th
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Development and Applications of a M
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Figure 3. Hepa-1 cells undergoing a
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cellular processing (such as post-t
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8. Initial demonstration of an off-
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expression systems for several year
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accumulation of protein products, i
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Fungal Conversion of Agricultural W
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Fungal Conversion of Waste Glucose/
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Summary and Conclusions We demonstr
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are shown in Figure 1(a) and 1(b),
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Anchorage-Independent Growth (% con
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selection of seedlings demonstratin
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NMR-Based Structural Genomics Micha
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Solution-State Structure and Fold-C
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Plant Root Exudates and Microbial G
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Summary and Conclusions • The gre
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Jones-Oliveira JB, JS Oliveira, HE
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• securely moves files to a targe
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Plenary invited lecture, “The Ele
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Figure 1. X3DGEN tetrahedral mesh o
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Figure 5a. OSO volume rendering of
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Development of Models of Cell-Signa
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SOS p 23 EGFR Professor Rodland at
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Oliveira JS, JB Jones-Oliveira, and
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Figure 2. Associating a dataset mar
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to 1) incorporate three-dimensional
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shown in Figure 1. Simulated result
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HostBuilder: A Tool for the Combina
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Invariant Discretization Methods fo
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Graphics, Inc., workstation. The co
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Mixed Hamiltonian Methods for Geoch
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guyanaite, and grimaldiite (see Fig
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in the Environmental Molecular Scie
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Simulation and Modeling of Electroc
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Summary and Conclusions We implemen
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Since the implementation of the gen
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asis of previous performance, perce
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Bioinformatics for High-Throughput
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Summary and Conclusions In previous
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User Cat - Supervisor (client) Anal
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The second goal was to research way
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Figure 1. A visualization technique
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and interactions. The flexibility o
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Figure 4. A filtered version of Fig
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Development of Modeling Tools for J
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Figure 2. Schematic of experimental
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Integrated Modeling and Simulation
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(a) (b) (c) (d) Figure 1. Results o
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Thurman DA. August 2000. Collaborat
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MARC Input initial m esh and result
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(Johnson 1999; Colligan 1999; Gould
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Source Building Release Observed re
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To illustrate the potential impact
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Non-Invasive Biological Monitoring
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Materials Science and Technology
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Figure 1. (a) - (c) Successive magn
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Component Fabrication and Assembly
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Development of a Low-Cost Photoelec
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elaxations, indicating the adequacy
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Promising chemical durability, as m
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Study Control Number : PN98028/1274
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High Power Density Solid Oxide Fuel
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Hybrid Plasma Process for Vacuum De
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Matson DW, PM Martin, WD Bennett, J
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We showed the proof-of-principle ap
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Bandgap Energy (eV) 2.9 2.85 2.8 2.
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Ultrathin Electrolyte Test Results
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Detailed Investigations on Hydrogen
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identified low energy step structur
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Development of Novel Photo-Catalyst
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-2 -1 Energy (eV) 0 1 2 Cu2O SrTiO3
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Here, we perform adsorption and des
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exceed those in the all-metal devic
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Intensity (a.u.) 0 100 200 300 400
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integrated voltage (V) 0.10 0.08 0.
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The first step was to implement thi
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Spontaneous Formation of Cu2O Nano-
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Actinide Chemistry at the Aqueous-M
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Figure 5. Close-up atomic force mic
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to 300°C and 400°C. Unfortunately
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Summary and Conclusions Transmutati
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Advanced Calibration/Registration T
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Figure 1a. Violet filter Figure 1b.
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Autonomous Ultrasonic Probe for Pro
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containing nitrous oxide, an optica
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Chemical Detection Using Cavity Enh
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Figure 3 is a color rendered simula
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appropriate study has been performe
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Exploitation of Conventional Chemic
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Figures 3 and 4 illustrate the impa
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enefits of using a modified spread-
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Single Channel Signal 0.0025 0.0020
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Therefore, in order to extract the
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A next-generation technology is nee
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Wind Dir. Figure 2. Negative ion co
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Volumetric Imaging of Materials by
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amplitude and peak frequency change
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Figure 3. 13 C NMR spectrum of corn
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Approach Two flow loops, one that o
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Modification of Ion Exchange Resins
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Permanganate Treatment for the Deco
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minimized by medium exchange. After
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Figure 1. Thermogravimetric analysi
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Validation and Scale-Up of Monosacc
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Concentration (g/100g solution) Con
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Analysis of High-Volume, Hyper-Dime
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ecent figure of merit values. Shoul
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Probabilistic Methods Development f
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a) Traditional PCA-based process co
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Acronyms and Abbreviations 2-D two-
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PNNL-13501 - Pacific Northwest National Laboratory

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