20260514 11063400 1.0 FALSE USFS_RMA_AK_Potential_Control_Location -20037507.067200 20037507.842788 6296425.496000 11542285.496000 1 Projected GCS_WGS_1984 Linear Unit: Meter (1.000000) WGS_1984_Web_Mercator_Auxiliary_Sphere <ProjectedCoordinateSystem xsi:type='typens:ProjectedCoordinateSystem' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/3.6.0'><WKT>PROJCS[&quot;WGS_1984_Web_Mercator_Auxiliary_Sphere&quot;,GEOGCS[&quot;GCS_WGS_1984&quot;,DATUM[&quot;D_WGS_1984&quot;,SPHEROID[&quot;WGS_1984&quot;,6378137.0,298.257223563]],PRIMEM[&quot;Greenwich&quot;,0.0],UNIT[&quot;Degree&quot;,0.0174532925199433]],PROJECTION[&quot;Mercator_Auxiliary_Sphere&quot;],PARAMETER[&quot;False_Easting&quot;,0.0],PARAMETER[&quot;False_Northing&quot;,0.0],PARAMETER[&quot;Central_Meridian&quot;,0.0],PARAMETER[&quot;Standard_Parallel_1&quot;,0.0],PARAMETER[&quot;Auxiliary_Sphere_Type&quot;,0.0],UNIT[&quot;Meter&quot;,1.0],AUTHORITY[&quot;EPSG&quot;,3857]]</WKT><XOrigin>-20037700</XOrigin><YOrigin>-30241100</YOrigin><XYScale>148923141.92838538</XYScale><ZOrigin>-100000</ZOrigin><ZScale>10000</ZScale><MOrigin>-100000</MOrigin><MScale>10000</MScale><XYTolerance>0.001</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><WKID>102100</WKID><LatestWKID>3857</LatestWKID></ProjectedCoordinateSystem> 8 FALSE None 1 AMD TRUE continuous signed integer 100000 100000 1000 20 2048 20 75 0.01 10 600 300 Basic 1 NorthWest,Center,LockRaster,ByAttribute,Nadir,Viewpoint,Seamline,None None,JPEG,LZ77,LERC Basic None,Basic,Base-Top Height,Top-Top Shadow Height,Base-Top Shadow Height,3D -1 -1 0.8 1 NorthWest,Center,LockRaster,ByAttribute,Nadir,Viewpoint,Seamline,None None,JPEG,LZ77,LERC None,Basic,Full NONE <_docversion_ Sync="TRUE">10 name,minps,maxps,lowps,highps,metadata 153600 30 15360 181163174 name,minps,maxps,lowps,highps,tag,groupname,productname,centerx,centery,zorder,metadata,st_area(shape),st_perimeter(shape) minps,maxps,lowps,highps,centerx,centery,zorder,st_area(shape),st_perimeter(shape) -1 1 1 0 0 0 0 0 0 20260514 11272500 20260514 11593400 150000000 5000 FGDC Benjamin Gannon USDA Forest Service, Fire and Aviation Management Spatial Fire Analyst (999)-999-9999 201 14th St SW Washington DC 20024 20260514 ArcGIS Metadata 1.0 USFS_RMA_AK_Potential_Control_Location 2026 2026 USDA Forest Service, Fire and Aviation Management, Strategic Analytics Branch USDA Forest Service Washington DC raster digital data USDA Forest Service. 2026. Alaska Potential Control Location Suitability (PCL), version 2026. USDA Forest Service National Office, Fire and Aviation Management, Strategic Analytics Branch, Washington DC. <div style='text-align:Left;'><div><div><p><span>Potential Control Location Suitability (PCL) is an estimate of fire control likelihood or probability developed by relating observations of fire control and lack of control with landscape predictor variables thought to influence control likelihood (O’Connor et al. 2017). Observations of fire control success come from the perimeters of historical fires during the period 2019-2024 and observations of control failure come from the same fire interiors. PCL is modeled using the boosted regression tree method, which iteratively fits regression trees to random subsets of the data to minimize model deviance from the training data. Boosted regression is ideal for this application because the models can capture non-linear effects and variable interactions. Landscape predictor variables include road influence, barrier influence, cost distance, flat topography influence, valley topography influence, ridge topography influence, steep topography influence, elevation, Suppression Difficulty Index (SDI), the SDI fireline creation sub-index, modeled fire rate of spread, and prior burning extent and timing. A single PCL model was developed for Alaska. This edition of PCL was modeled using 2026 capable fuels data. See the full methods report for more details.</span></p></div></div></div> The purpose of Potential Control Location Suitability (PCL) is to provide information about potential control opportunities and challenges for large fire containment strategy development, incident prioritization, and other broad scale fire and fuels management planning tasks. Data users are encouraged to access the methods report for more details. Richard Stratton USDA Forest Service, Fire and Aviation Management Strategic Analytics Branch Chief (999)-999-9999 201 14th St SW Washington DC 20024 richard.stratton@usda.gov ISO 19115 Topic Categories biota environment society biota environment society <div style='text-align:Left;'><div><div><p style='margin:0 0 14 0;'><span><span>The USDA Forest Service makes no warranty, expressed or implied, including the warranties of merchantability and fitness for a particular purpose, nor assumes any legal liability or responsibility for the accuracy, reliability, completeness or utility of these geospatial data, or for the improper or incorrect use of these geospatial data. These geospatial data and related maps or graphics are not legal documents and are not intended to be used as such. The data and maps may not be used to determine title, ownership, legal descriptions or boundaries, legal jurisdiction, or restrictions that may be in place on either public or private land. Natural hazards may or may not be depicted on the data and maps, and land users should exercise due caution. The user is responsible to verify the limitations of the geospatial data and to use the data accordingly. As a work of the United States Government, these data are within the public domain of the United States.</span></span></p><p style='margin:0 0 14 0;'><span><span>Additionally, the U.S. Forest Service waives copyright and related rights in the work worldwide through the CC0 (which can be found at </span></span><a href='https://creativecommons.org/public-domain/cc0/' style='text-decoration:underline;'><span style='text-decoration:underline;'><span>https://creativecommons.org/public-domain/cc0/</span></span></a><span><span>).</span></span></p><p style='font-weight:bold;margin:0 0 14 0;'><span><span>Non-Discrimination Statement</span></span></p><p style='margin:0 0 14 0;'><span><span>In accordance with Federal civil rights law and U.S. Department of Agriculture (USDA) civil rights regulations and policies, the USDA, its Agencies, offices, and employees, and institutions participating in or administering USDA programs are prohibited from discriminating based on race, color, national origin, religion, sex, disability, age, marital status, family/parental status, income derived from a public assistance program, political beliefs, or reprisal or retaliation for prior civil rights activity, in any program or activity conducted or funded by USDA (not all bases apply to all programs). Remedies and complaint filing deadlines vary by program or incident.</span></span></p><p style='margin:0 0 14 0;'><span><span>Persons with disabilities who require alternative means of communication for program information (e.g., Braille, large print, audiotape, American Sign Language, etc.) should contact the State or local Agency that administers the program or contact USDA through the Telecommunications Relay Service at 711 (voice and TTY). Additionally, program information may be made available in languages other than English.</span></span></p><p style='margin:0 0 14 0;'><span><span>To file a program discrimination complaint, complete the USDA Program Discrimination Complaint Form, AD-3027, found online at </span></span><a href='https://www.usda.gov/oascr/how-to-file-a-program-discrimination-complaint' style='text-decoration:underline;'><span style='text-decoration:underline;'><span>How to File a Program Discrimination Complaint</span></span></a><span><span> and at any USDA office or write a letter addressed to USDA and provide in the letter all of the information requested in the form. To request a copy of the complaint form, call (866) 632-9992. Submit your completed form or letter to USDA by: (1) mail: U.S. Department of Agriculture, Office of the Assistant Secretary for Civil Rights, 1400 Independence Avenue, SW, Mail Stop 9410, Washington, D.C. 20250-9410; (2) fax: (202) 690-7442; or (3) email: </span></span><a href='mailto:program.intake@usda.gov' style='text-decoration:underline;'><span style='text-decoration:underline;'><span>program.intake@usda.gov</span></span></a><span><span>.</span></span></p><p style='margin:0 0 14 0;'><span><span>USDA is an equal opportunity provider, employer, and lender.</span></span></p></div></div></div> The USDA makes no warranty, expressed or implied, including the warranties of merchantability and fitness for a particular purpose, nor assumes any legal liability or responsibility for the accuracy, reliability, completeness or utility of these geospatial data, or for the improper or incorrect use of these geospatial data. These geospatial data and related maps or graphics are not legal documents and are not intended to be used as such. The data and maps may not be used to determine title, ownership, legal descriptions or boundaries, legal jurisdiction, or restrictions that may be in place on either public or private land. Natural hazards may or may not be depicted on the data and maps, and land users should exercise due caution. The data are dynamic and may change over time. The user is responsible to verify the limitations of the geospatial data and to use the data accordingly. true -179.23100 -129.96000 71.44000 51.17500 Ground condition 2026 Esri ArcGIS 13.6.3.59530 1 -179.999989 179.999996 71.405263 49.126925 USDA - GEO; USDA - USFS – GTAC A PCL model produces estimates of fire control likelihood or probability, but it is important to recognize that this implementation of PCL does not capture detailed weather, fire behavior, and suppression activities in either the training data or predictions. PCL provides useful models of how landscape factors such as fuels, topography, and accessibility influence potential for control under typical large fire conditions. The Area Under the Receiver Operating Characteristic Curve (AUC) is 0.646 as calculated using 3-fold cross-validation. PCL prediction accuracy also depends on the accuracy of source datasets and methods for mapping the predictor variables. PCL results were reviewed by the data producers and owners to confirm that models were developed and applied as described in the methods report. PCL was confirmed to contain non-null values for Alaska. Generate random sample points within historical fire extents to a target density of one sample per two hectares and a minimum separation distance of 60-m. Attribute samples that fall within 60-m of the perimeter as successes (1) and the remaining samples that intersect the fire extent as failures (0). Extract predictor variable values to the sample points. Train PCL boosted regression model. Predict PCL boosted regression model to contemporary representations of the predictor variables. LANDFIRE fuels and topography LANDFIRE fuels and topography 2025 LANDFIRE United States Department of Agriculture and Department of Interior Washington, D.C. raster digital data LANDFIRE. 2025. LANDFIRE: fuels and topography. U.S. Department of Agriculture and U.S. Department of the Interior. https://landfire.gov/ Ground Condition 2016 2025 0 1 0 -20037507.067200 6296425.496000 -20037507.067200 11542285.496000 20037507.842788 11542285.496000 20037507.842788 6296425.496000 0.387794 8919355.496000 1335834 29.999996 174862 30.000000 Potential Control Location Suitability (PCL) PCL represents the potential control likelihood or probability under typical large fire conditions O'Connor et al. 2017 EPSG 6.18.3(9.3.1.2) Band_1 99.000000 6.000000 8 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MJ5oYFsjacE4rl/H0UMOgWSbSjC4/dqvT7p3Z/Cvl6WZ4jFVqVOvo09rW+//AIY2w1NKpdK6XVM87ooor6s9MKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACikJwMngCigDHoooqiAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA0NEbTk1iB9WDtZLuaRVGS3BwPzxXqPhTXdL1y4ltrHT/sTW6B9i4xjhQRjoa8ervfA/iyx0mzi025jYzTXO1ZAoARWxyW789q8fN8PKpRc4JuW2/4mVSlGW6v0PTDFDAyMI3ZslQRliM8/lUF3qH2SGST7PK4j5bGOnHT39qtyEoVCjndhh7etRyWsMzq8iBtucA9Priviac4KSddXRxVlyXhTik7avt/Ww+3nE8O/y2Q9NrYyPyqpqV3fWpjNpaCdVBecEkHZ/s9i3tV5QobIABPU08uFUs2FUdSTxUwnD2t1G67HpYaEMRS/eT5bdv1I41/jy7b8Nhj93joKkAwMVHb3MN3EstvIkkZ/iBqas5pqTUlZnqYHCUrOUXfzGO/lIXZWIHZRuP5U2VHf5V+UH+IHlae0gVgOcn2qGaV9sm1441C/LITnB9xTirtE4zFYaMORyu09lvfsL5UeSTklmDsRxlh3p7EMNp6HiqkwK7XkmbhRwF+Ut6+v4VYGXZD8jY7+/t+tXJNJSueHHMalWbgtLsz10ZxboqX00UiMzblwVYnuVPU/jV8QEmD53Ih9T944xk1KThs4qk9tqU11DIb1IYkbLxRx58xfQk9K19rOu26k0vX/AICPTwtNUHy0E77adfVvQvFwXEe5dxGduecVRWa1tXknmuUZhiN5M859wOKfdXE1sgEVtJczY6quAKx57RdWuJmt4njl3DzBKoBQ4PzZ98dK0w9FNNzdovc9qnh3NXqvrc31uYpIVljPmIwJBQZqUVSt4l0y0ggRAWZtrEHALdzzVkmUSjGwR4ySeST6Y/rXNOCT93Yy9vT9pKG/Lu+3Ylqq13i/S18s5I3byRjGD096bdajDZPEkrAGRscn7o9TWbYxw6lqs18ZNxhf91Hv54PB+la0aHuOpNaW/E6qTVWHM1b1C9u5tKDgsd0sm5Tu3EjHOM/d/GpNKzqMcdxcvFK0e4YEe0qx9T0NV9fgS6hjvIXV2RcOquDtX1/M03Q0ln0+WKFhGfN+dmydyEYIHoa9Bwj9V9otJdWaqCjD3NDoUiSLPlxomeu1QKQiUSbg25T/AABQP1pgjRIoYTI42kbcPy2PX1p0hV28re8bMOoH8j615PUwU05NRd+/kFxAlxGI5OY8/Mv96sDxjYJceGhaoJABJGIwgzzngH2963ZJQoaN1lUBNxkHT/vr1rH8WSzjw60kEUhyy70CHeFPXGOhFd+WOccXS1tqjizWLeEnqefQ2FnP4muEsAmLW3eXH8O9e4z6cH8KfP4mn1TQYxPeE3MM8bFefmHJ3H6H8uK1/CGhNbRyve27xjy2ZZlwpZGUrsbP5j6VzV5bS6PrSQQ/Z4vlaILMnCoT0cnq2K/RoVaOJxMqb95wSs9PmfE8uiv8jWW9uLiDUJLU/adReDy5rorhCv8AEqn+InrXFDjGOMV6+NKA0m1ltoIReRwb2SKPCNk5OB6Z7VwF94c1GZ457eyJeYbmgi6ofZfSryvH4eo52stfy/QKMlCXK9nsdL8P9Qu7uYRS3MRih3KUfO98jg7s+vHTpXovSvMPBuhX4S6a50hWjZkZGuJGi+Zcnjb15r0aG1eK4kl+0zMsnPlPgqp9uM18PxLGjLGylTl+X6fqfR5M5wbUYXT6lmmseMDGfekkljiAMsiICcAuwGa5v/hO/D/2t42v41SNchtpIY+2BXhUsLWrJunFux6ePxEaSUb7/l1OkyVAYrk46Cqt3qljYttur23hcjISSQAn8KwP+Eu0y7mJt9VZWkiKpAIiWD9ucU/QfDFoGfVNQcanf3O13mnRSFI7KO2On4V1SwKox58RdeSXX5nnKvzTdOldXd1/n3+86E3G5mVVY7QcuVIUHH6/hXKa5Yarqmx476N7WZfKltUGBt3DL889s+2BXQX2sWdjdQW80qied/LjjzzuPTPoPeucXVLzVrySeBZLOz84QySxx5ZSAdxf24AB969DKMPWhL28YpJdXrf0/wCAeNj60nJpyvY4W4uptOvrW7t7h5o4Zi1q0wzlFxg5POOelMbxPrDxlHvZGGNqEnmMf7Jo8Q6hJe6gY2lhlSD5EeKHyhjuNtVV06T+zHvX+RAcKT/Ef7v1xz9M1+lU6VFwjOtFc2xhGKcVzIb/AGndmNIzLuRAQAyg5B6g+tWNM1e4sUmjW6kiRoyAFGcnBwPbk5zWZRXXKjTlHla0NHTjax7L4V1I3+mW7nakRTYsZXDEjqR6jrzW9AqW8SxRkbEGOe31rw2wvtWtdjWUtxhQdgXLAc8kD61uaXr9/czs+oXc5geRVlEPD7hyMgeuMV8NmXDE51Z1Kc0ovW3mTSqzw2sLaP5nrSMGQOjBwwyCDway/wDhILA6oNOimV5j97byF/H61S0u5vHsp7kLDPM7FoQpKCVfQIcYOMVRWb/hHrW9v9UtGht5rlGijgUMY2ZfmJYds8fhXz9LK4RlOE/eeySdrt/5HRWzOpOnGFJ8vludeDkEHOKwNR8M29zKbm5mubvajKsM0pKfMeM+wqk3iye/vobHTLZ986pJBMVLqy5wxYDoo6ZrpoLlLqESWk8UqbtpZTuGR1H1rBUcXlklN6N/edcZ0sVTtUTdlvs1+Otjy6X4e6o0ElxBcWc3JKpCxx16c1WsfC4uI3huEmhvYxuKqwcOO3TpXrMLI0z7ChC/dw/5/L2p8kSOrRbdoZWGQvQHr/n2r2ocWYqN4VFfz6ow+ozrU+ajO5wBsIdPtGg0m6ke4uyCI2iJRIlGcP2HPccnNchqmpNNfecvlm6RubmHKhuMYA/rXf8AizTLi4ureKwF75piCjyx+52g/Nv9TjPSvMbm2ktbqS3cEvGxB4NfVZJWp4mn7Vyu3rruedCFpvn3NjRfF2p6K21ZftEBIzHMS23/AHfQ1d1fxpd+IbQWc9pbxIHDgpknj3NcoRjrUtv/AKz8K6sXlmEbeI9muddT0KE5RfKnoy1RRRXnHcFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUx5PLwSrFe5HanAhlBByD3oAWiq8115MmzZn3p6XEcnyo+GPqKAEu2227f7XFFUbjeshR5C+KKZLZFRRRTEFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFKVZcblI3DIyMZHrSUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFOjdopEkT7ykMM+tNootcZ3V34+ur7VtNu7C1kS6j/dSQlsrODj9c5+nFel2E93dQCS5szalv8Ali5y6/71fPsM0lvPHPC22SNg6H0I6V63pNhHqWkRateTSIbxEeSTzXLSNz97bjAB6Y7cGvl84wNCFOKSstr7s560Jzdo3bb+865ECSSMBzIRkAVzOppbanrto817i2tZOVDYUn+63rz/ADqPXby90vw+09rJb3EduFWE/vGMijg7iDjPX8O9casGqr4Yk1e4soxp8k/2guJtsmS2BtHpnpWeUYOnC9WpOzfur5mFXCYqKUGrfnbqerXN0mmxKkNo8xJOIbcDcfXA4qO5vdRimj+zaclxG6qSDOI3jPfdnjH0rlvBWu3OtrJHPH5wiG1p3YmVQxP3mrsXhaN98QBZUKqh4B/GvLxVCOFreyqJOXe+99uuhr9exfK4x0in06IwRrN3cazcWv2eLdAdqPG+Xz1KkH271vmPd8wGzK9MZwT3rF0gNqs0t7d6S9hdQz5CuCN4xjfnjceoreEe1enBOanMalOMowpx5Wlr11OOOHxFWUpSTd9b/qRwEyKA6kOOefyzUwAHAGBSKFjQKgAXsB2pwHHFeZJpt2PXwWHgoqEUnLqKAPSq9szSSXEvmFl37EUrjbt6/r3qvqtzdW6xtbLnn5hkc8f0qDRBKTKxEipxkS5JJxnrn+g611RoWoupc+mhRjCFlp6Gupzx39KyJr27N2y20MZPnhTJ6rwOfUZJ59q1giCQyBQHIwT3xTFt40uTOow5TZx0xnNRSqQhe6uKgpRhyz6EmOf8ar3l4lnGGYEs3CjtnHrSXUkx/c20iJIMF3ccIpzz71S1UxLapDcTySPsw6IASw/v47Y9aqjRUpx5uo40kYiWr6g8155skmHUAFRvkbHQVpWl5b6fBMk0EiXQcNsOA8hJz27e1VXsZZLuC3sBGVhRZd+R8xPcnoTW/eabbXzRmZWzH90q2DXp4mvBcsJ/D5bo1nJppW0/ExbfTZJFmneOaOOcsBEiDIXk/NnHfGK1NKkkiiFlNG4eJflfZhWXtz61SuTeqNSS2vHlMe3CcllB6jPrS2r3jxrCtzO8E23ZMOXj7nd6egrKqnVg+ZqxT1RctismrTsblJHjG3A/unt6cH8a0etcy0ckGsYt7W3RrVSwCtjzARxnPet6dEdIXlmeIqQw2Njn+vWuXEUbOLT0aIcUnoSmNNxICq57gDNLJGJYmjP3WG01WgFtLHIYXErMSGYtznODz26VJJbJLcCZmfIUpgNgEH29a53aMt3oc04RqRcKvX8ijfTx6bprIiGUW6BmBwcDPf3Pb6V57BG2ta1JdHVLcpBh45rhBIig5yrLjBb/AArrPGGvQaKESOItqEyYj2nqvTn1/wDr15YftWqXbCKDMj/eSFSASPbpX3/DmDlLCyqy0cvtM+NzCCeIaiklFWOr8U+JgkNtplhJFJ5IDNOibQejABRgYx1GKyNDvLm6103TiYvjDy2/ymMHjOB1z0p2leGVu7RdQurnyLKLJvDIhUxjtt/vZ9q6bw3o2lXd8l5pd/cyWdsS32eSPa2769+eRXoVK+CwOGnTgr2Tu7dfXz+45JJNcq32udtYxSQ2cMbSSMRyXkIYkelRajrul6Q8SX97HA0nTcen19KnvJ1itGH2mK3ldSsTysAAxHHX3rwbUxqN7r08Ny8l3feaYuBuLEcAKP5V8Nl+AWPqSqVHZbn1FLEPDUYwi00upteNfFg8RXcUVqHSyg5XeNrM3cnn8vrWRo/h3VNe8z+zrXzVi4dywVVPpk966LQPhzqN/Ik2p/6HbBgWjb/WOvXj09OefavU7OzttOtI7SzgWKBBhVX+vqfevUxOaUMBTVDC2bX3HNJ88XVlLf53PDLXUL7RNQ+zuNzW7NF5LfMFYnnH4169pPiKz1AW1oJBZ3i/620nXa+AvPHanS+EdJn14azJFI11vDlWbKMQMZ2kf5xWncWdlcORcWsLyTAhmMYLEf71cGPzOhiowXK72v6P0NI4Jx9+Wn6o5HxBpl7eR3UVvbnUJ45sx3Ltsa3B/gHdsdfxrltcu9S0nT7fQpBNCpzJM7gfvGJ5KsOq/rXpkWnQQtNIZHikkwZsys5ZV+7yTx/kVyGvQ2OvawklvAt/OuEIt7vAfr8uD0PH0969nKMyTapzjeEdb22dtPLvseFUoqE1Fa31t2MS28N2cF3bm6vhIk+77KFU5mcDK/KR91un410d7Z2sWh6hDLarLBCwkmYMVCzNhQFBPCrnp6VQto5JPFU3iDWbf7PbWDrbxpCxciVQAmePmz03evFZGveLbqU3enfYZYTNAlvPHcEh96tkPt/hJHGK0nicTiqqtJu1uui9LdkejDDrlV36932NzUfCGm6JZXV3DelnTyyA7K3yMQCQMcE87TT/AOwbW3uZNGsRHeXZhD3MjwAlMn5XyTz6ED3Ncjq3irUdQtjYX9paq8IVFKxlZIyowOc8/jWj4O1xrRlgs7Ga71ie4yzDb+8hx8ykk9eCRVVJ5hGg5SqXf4fNgsHR5tLv1/I7S10uytI5LK9vIN8u+KNIY/LWNmySV7nr39Kqx2eg+E7Rp5r6eS5C7WljkIZwW9BxmtBrq3v7y5i1i3g0/wDcbIhNMDMofPIxwvTPesfxR4RsZbeE2bzR3ECxxkvyhj3Y3MfXknPtXn4auqlRUsTUklK17bP59jjrYSpQ96VrNvbcpvp1n4puVvrbXpHhjBZ7W8Y70Gf9noORzWze+HTNoMVj572ryZWOKK4LxMewZjycjt61xepaJL4baS5XUFSXcY4YwMyMpH3j6KecGp/Ct7Je3E1hdGe4Ev71ADko453ryOc44r3q2DqOkq+Hq3hDVaf8Nc5JNWclsdxa+H5rTw0tmzebcwxNCrW7CNpIyc7dxHy9TWPFoviO20lbW1kQpbvgIzGJnGQcblOCc/pWjq9rcrm60q/mj1ERsxTGY1XGWYqeFJ45Oa5y8+Ik8kMSxWqmVRtlDndG/qfx55rysDRx1dOVNRkm7u62fp+RScKiV9/LY6CXxRNp84S5tfLjLqkaklpZTnBwD/COee9akPiS2knSC4jlt/MRNrsDgs3G3d9eM15zc+L2leIJagRw/wCq3bWZQfvKSwbI9K6PRfGNpNutoLH7JEqDMu4bYxxwB+da47If3amqPveT/r9SYVq9LW7t951c8jQ3gKXUDCGNi8b7t+PXg/0rntXsLG9b/iYNb7rpgyzw4iIUdM7m5Ptmo9QLq8ms20d8ktyjL5SQBg6/dHDdM8ZA61yGo2mradYzHULHy4bxAyJ/zxKnj5eq/wD16eVZdytSjU5Zbdn5/d+JMpqqrQSjqL4g0e+OpbooxNGwCxRxYLBQP7g6CsVInguWilUo68MprR0/xFd2UE0byyvmIrCwPKP2Oe468VnRyNLctI+NzcnAwK+slGtGjKM7WS37nThVOMlGWyJ6KKK8g9YKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAqo8E0bsYGwrdvSrdFAGVJFKvLq31NR1s1Wks435X5T+lO4rGeTnmirf2BsffGfpRQKxUooopiCiiigAooooAKKKKACiiigAooooAKKKKANd9OnvNBhv4rqS7eEmKS3HzG3iH3T7DOfp+NZFWtPv7jTLxLm2kKOvXH8Q9D6itE6joVz5puNIkhkl3fvLeXCxHAwVXv0JIJ71gnOm3pdeQXd7GJRTn2h22ElMnaSOcds02t0AUUUUAFFFFABRRRQAUUUUAFFXk0XVJLRLuPT7l7d9xWRIyw469OlbfhzwTda3B9tuJPstir/ADFlYO69SV4xjtmueriqNKLlKWiNYUZzdkjls4or2WDwLolpG90Igrvnmc8Qoy4I29CR1z61wuqeCWsNFm1OC/E0MDEMHiZSV3YBB9emR+tcmHzWhWlyq62XzZtUwVaCba0KegWOkMq3+qXsLJHJtNichpPcn+79OeK9Z0u+g1fQ0axtAlmreUI3YqGVf7pFeDg4II6ivSdG+JcS2flatCzXIIVHiUKhH+16fhXHnGDrVYqdNczT77fIVLFSpK0Iq512rJE2neTeOVtdu6QBBg+isB/CPamXunnV/DJs45/sYuFC5iAkQrjoB2U47U++try91SxB8l7ARebLgtgt/Dt/TrXL+Idc8QW9lDdQLNZJGNsmYlWNyTwV5Oa87B4OeIUKdKSUr31ei36d9Dxq+IqrESlN66pdjq/Dnhyy8O2Rhtt7PJhpZH6sQP0HtWw54rnfB9zNfaJHfXMzPNISWXBCgZ469/ccV0Dok6APhlzkV42PhOOJlCpK7Ts2ephZyrYWVJWUylqOoNp+lXV68TbokYqg+Ysei9PU4rLtbuKwhM8t49zdyYFyrMVVG29k6L2zUd3Otx4mj0K8nt1siqzwxLGwaRgT+7Jzg4+9+VbVzZx3V0rzxfLH0OR8/wBeK6qSo0Uo1lpLXTt0RxYqhVhRShL9fUfp8/2y0ju84jlUMilSNv581cBB6HNJwq8DoOAKyobmS13xm3mRppWkXzI8j5u3ynrXByKrKUoaeXkfU4PDRo0lFa/qW7+yju1R5HZPJy24AH+Y9qthtwB655zWOZ3upIZYXkLrtXeAVifd1xWuBtAAycdz1oqxlCKi2dMk+4tRmZMvyPk+8T2pklwn2lbYMRIy7/wHaskLFZWwN5PDaoJHYCVlAkLHnPr3q6OF51eTs+i7nz+Z5pOnJQoff59jbHzryAVP5VR1axa+tdkb7JQeOcA/WrEZLW6JNiQP8oKLgEY7jtTIYxb/AGlmTYm/cCCTkbRzUQ5qcrxeqPWp1JuhGcNXpuFjY/YQyrLuRgvy7AMEdTx1q0BgYyT7mq1o0jBpZJUdJfnQA8KPQe2Oamk3shEbBHz8u4damrzSneT1Ot6sXyo/M8zYu/GN2OaRFZOHmLlumQBj6YoikQ7Y9yCQKC0YYfLUUUskr5eMDDEKvf68/wBPWlaWqYWZBf6Ra6hhpcxuOS69T9c1hG3bUZo7bzne6UkSSONqqg6YrqJo4ZwI5kVlyCA3c09yU+faWH+yuTXTRxc6cbb9vIIycIpbjIYFt1VE5GMZPXFPycfKKGxgnj3OelUb3U7bTeZ5d0kjBIoVGWZiOFH19a5owqVZ2irs8vF15RnZqy79jK1eznd0meGO8uEIaIR/LLjp0Py4565qnYaZBZWC6hc27Cf95JvaUDaT0Xj5W6VYbVdSuIJ5PsEULquyW0kmBZVPAZj+fA9K5SSK81rXX8PzS3Jt1k3ylEOV4yAVY8Dt+NfaYGliJ0eSpNRUdXZ9Oq09T4qor1Hf5jdZu59bs0k1+6utPZDuiRotySZA6AdD3yfX2rpvCMWn6Xo11dxSOLVBvknZG5wBkgHnH4Vcj2T6XHJrSQpFI32dLXqgIO1fm9eM1Q8Y+KLHStEextjHPPcxbEQjKhOVJz7Y6VjicY8RTWBpQtd622Vu2h6WDw0qslVm9Fr6/wBepwniPWLzxZf3FxbRv9jtYx8m/jbnG/B9c9KwbC6ax1C2u0LAwyq/ynB4NRLIyQtGpwr43e+K0PDumjV/ENjYurNHJKPM2nB2Dlv0r2eSFCi19lI7Um3oe+I5cbjhQ3zKB1wR3p/CjNZtz4g0qw1KLTLi58q5dV2KVOOeAN3Sp9U0u21e0FvcbwFcSI8bYZGHRhX546DUk6qcYy2dj0vZXTlBpzW3S39ehaBJFMYnJb5S2CEBOM/5xWZFPBpEr297qJ+Zd6PczDLDux4AqxF5DvHdR3RkSX5Nwwdx9vT8K0lhJU3fp0dnZni1MbOceVNt7PUxvFGqnT9BvGEzNceX5fllcAbjgnOOev6VxeiaVf2F0n2y0IKHfBHIQgkIwx+fPAGQa7K/mtri6urDWfKVgPM/dOwVYTxvZm4z3wBXnGrazNqYTTo32abDIRbxtkkDPDMeSTzX3GTUJvDOjTja+rbWlraW/r5nnJS52n8zvdZtdZ1jSptO0SCO1srXaqJI4LysCGARgTt2n1NeZlbnV57/AFG+ucvCu+aV+rt91VA7nOPwya9O1i6bQPDllDNDJJbpFh7i0X5FkwVAOCPvFs5Pp6mua15o7L4c6Lp+d00/zAwkFHZWO7d6nkY/GvOwFSVNcsUmnKyfXzf4H0KvUgpSfS9jj7/UJNQkjeSG3iKIEAgiEYPucdT702wivZ72OHT1ma6kyqLD95uOQPwzW/oPhT+1Li/VnE8Fvb71nik2RlyOm5h/D37cUeEb5dE8R3AeewVVVkNxNllXaeqEev6ivVniIqE40ldxWxHJJtOX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