Genus 2 curve data - 10000.b.800000.1

Formats: - HTML - YAML - JSON - 2025-12-16T12:26:36.417103

• g2c_curves •

  Column	Type
  Lhash	text
  abs_disc	bigint
  analytic_rank	smallint
  analytic_rank_proved	boolean
  analytic_sha	smallint
  aut_grp_id	text
  aut_grp_label	text
  aut_grp_tex	text
  bad_lfactors	text
  bad_primes	integer[]
  class	text
  cond	bigint
  disc_sign	smallint
  end_alg	text
  eqn	text
  g2_inv	text
  geom_aut_grp_id	text
  geom_aut_grp_label	text
  geom_aut_grp_tex	text
  geom_end_alg	text
  globally_solvable	smallint
  has_square_sha	boolean
  hasse_weil_proved	boolean
  igusa_clebsch_inv	text
  igusa_inv	text
  is_gl2_type	boolean
  is_simple_base	boolean
  is_simple_geom	boolean
  label	text
  leading_coeff	numeric
  locally_solvable	boolean
  modell_images	text[]
  mw_rank	smallint
  mw_rank_proved	boolean
  non_maximal_primes	integer[]
  non_solvable_places	jsonb
  num_rat_pts	smallint
  num_rat_wpts	smallint
  real_geom_end_alg	text
  real_period	numeric
  regulator	numeric
  root_number	smallint
  st_group	text
  st_label	text
  st_label_components	integer[]
  tamagawa_product	smallint
  torsion_order	smallint
  torsion_subgroup	text
  two_selmer_rank	smallint
  two_torsion_field	jsonb

  
  {'Lhash': '1348264561008612918', 'abs_disc': 800000, 'analytic_rank': 0, 'analytic_rank_proved': False, 'analytic_sha': 1, 'aut_grp_id': '[2,1]', 'aut_grp_label': '2.1', 'aut_grp_tex': 'C_2', 'bad_lfactors': '[[2,[1]],[5,[1]]]', 'bad_primes': [2, 5], 'class': '10000.b', 'cond': 10000, 'disc_sign': 1, 'end_alg': 'Q', 'eqn': '[[1,0,0,0,0,1],[]]', 'g2_inv': "['0','0','0']", 'geom_aut_grp_id': '[10,2]', 'geom_aut_grp_label': '10.2', 'geom_aut_grp_tex': 'C_{10}', 'geom_end_alg': 'CM', 'globally_solvable': 1, 'has_square_sha': True, 'hasse_weil_proved': False, 'igusa_clebsch_inv': "['0','0','0','1']", 'igusa_inv': "['0','0','0','0','800000']", 'is_gl2_type': False, 'is_simple_base': True, 'is_simple_geom': True, 'label': '10000.b.800000.1', 'leading_coeff': {'__RealLiteral__': 0, 'data': '1.0314071041733177562983179141216861078043261206521280590', 'prec': 190}, 'locally_solvable': True, 'modell_images': ['2.180.2', '3.1296.1'], 'mw_rank': 0, 'mw_rank_proved': True, 'non_solvable_places': [], 'num_rat_pts': 4, 'num_rat_wpts': 2, 'real_geom_end_alg': 'C x C', 'real_period': {'__RealLiteral__': 0, 'data': '10.314071041733177562983179141', 'prec': 100}, 'regulator': {'__RealLiteral__': 0, 'data': '1.0', 'prec': 10}, 'root_number': 1, 'st_group': 'F_{ac}', 'st_label': '1.4.D.4.1a', 'st_label_components': [1, 4, 3, 4, 1, 0], 'tamagawa_product': 10, 'torsion_order': 10, 'torsion_subgroup': '[10]', 'two_selmer_rank': 1, 'two_torsion_field': ['4.0.125.1', [1, -1, 1, -1, 1], [4, 1], True]}
• g2c_endomorphisms •

  Column	Type
  factorsQQ_base	jsonb
  factorsQQ_geom	jsonb
  factorsRR_base	jsonb
  factorsRR_geom	jsonb
  fod_coeffs	jsonb
  fod_label	text
  is_simple_base	boolean
  is_simple_geom	boolean
  label	text
  lattice	jsonb
  ring_base	jsonb
  ring_geom	jsonb
  spl_facs_coeffs	jsonb
  spl_facs_condnorms	jsonb
  spl_facs_labels	jsonb
  spl_fod_coeffs	jsonb
  spl_fod_gen	jsonb
  spl_fod_label	text
  st_group_base	text
  st_group_geom	text

  
  {'factorsQQ_base': [['1.1.1.1', [0, 1], -1]], 'factorsQQ_geom': [['4.0.125.1', [1, -1, 1, -1, 1], -1]], 'factorsRR_base': ['RR'], 'factorsRR_geom': ['CC', 'CC'], 'fod_coeffs': [1, -1, 1, -1, 1], 'fod_label': '4.0.125.1', 'is_simple_base': True, 'is_simple_geom': True, 'label': '10000.b.800000.1', 'lattice': [[['1.1.1.1', [0, 1], [0, 0, 0, 0]], [['1.1.1.1', [0, 1], -1]], ['RR'], [1, -1], 'F_{ac}'], [['2.2.5.1', [-1, -1, 1], [0, 0, -1, 1]], [['2.2.5.1', [-1, -1, 1], -1]], ['RR', 'RR'], [1, -1], 'F_{ab}'], [['4.0.125.1', [1, -1, 1, -1, 1], [0, 1, 0, 0]], [['4.0.125.1', [1, -1, 1, -1, 1], -1]], ['CC', 'CC'], [1, -1], 'F']], 'ring_base': [1, -1], 'ring_geom': [1, -1], 'spl_fod_coeffs': [1, -1, 1, -1, 1], 'spl_fod_gen': [0, 1, 0, 0], 'spl_fod_label': '4.0.125.1', 'st_group_base': 'F_{ac}', 'st_group_geom': 'F'}
• g2c_ratpts •

  Column	Type
  label	text
  mw_gens	jsonb
  mw_gens_v	boolean
  mw_heights	numeric[]
  mw_invs	smallint[]
  num_rat_pts	smallint
  rat_pts	jsonb
  rat_pts_v	boolean

  
  {'label': '10000.b.800000.1', 'mw_gens': [[[[0, 1], [1, 1], [1, 1]], [[1, 1], [1, 1], [0, 1], [0, 1]]]], 'mw_gens_v': True, 'mw_heights': [], 'mw_invs': [10], 'num_rat_pts': 4, 'rat_pts': [[-1, 0, 1], [0, -1, 1], [0, 1, 1], [1, 0, 0]], 'rat_pts_v': True}
• g2c_galrep •

  Column	Type
  conductor	integer
  lmfdb_label	text
  modell_image	text
  prime	smallint

  
  
  • id: 3746
    {'conductor': 10000, 'lmfdb_label': '10000.b.800000.1', 'modell_image': '2.180.2', 'prime': 2}
  • id: 3747
    {'conductor': 10000, 'lmfdb_label': '10000.b.800000.1', 'modell_image': '3.1296.1', 'prime': 3}
• g2c_tamagawa •

  Column	Type
  cluster_label	text
  label	text
  local_root_number	numeric
  p	bigint
  tamagawa_number	smallint

  
  
  • id: 3
    {'label': '10000.b.800000.1', 'local_root_number': -1, 'p': 2, 'tamagawa_number': 5}
  • id: 4
    {'cluster_label': 'c5_1~4', 'label': '10000.b.800000.1', 'local_root_number': -1, 'p': 5, 'tamagawa_number': 2}
• g2c_plots •

  Column	Type
  label	text
  plot	text

  
  {'label': '10000.b.800000.1', 'plot': 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M2fLy1a5J3IIZnJHF27ml6%2BW2%2BVata0rk6gpAiAAADHOnBAWrZMWrpUWrzYu0BzjqpVpZ49pZtukm64QXK7LSkTKHUEQACAI2RlSZs3m/13V64027Dt3Zv/OcHBUocOUvfuJvC1aSMFBVlSLlCmCIAAgICTnS3t3CmtXy%2BtW2cmbaxb592JI0e5clLr1tI115jbu126SJGRlpQM%2BBQBEABga0lJ0pYtpndv0yZp40bTTpw497mRkVL79lLHjlLnzuZI4IMTEQABAH7v1CkzQWPnTrPY8vbtZlbutm3SoUMFf09oqNSqlbmN27at1K6d1KwZt3QBiQAIwALLly/XK6%2B8onXr1ikhIUFz587VrbfeeuFv3LBBeu01acECc/3II9Lo0VLTpmVbMMqUxyMdPSr997/S/v1mYsZvv3nbnj3S778X/hr16knNm0stWkgtW5rg17SpGdMH4Fz81QDgc2lpaWrVqpWGDBmiO%2B64o2jfNG%2Be2WIhI8P72LRp0uefm0DYsWPZFIti83ikkydNqEtKMu3IEenwYdNbd%2BiQdPCglJgoJSSYY3r6hV83Kkpq1Mi0Jk1MwLv8cnNk8WWgeFgIGoClXC7XhXsAMzKkunVNapCUIsktKVlSlGS6fjZvLvtiA0xWlnT6tHl7T5/O306eNLddT50yEydy2okT3paaatbJS0mRkpOl48fN8dgxs4hycVWrJl16qRQdbVq9eqY1aCA1bGiWZHG5Sv99AJyIHkCgjHg85h/B9HTvP6rp6aZlZJhjZqY5zzmeOWNaZqb3/MwZ8w/12S0727S859nZ5r979jFvy3ksp8bCjmefF/ZYUd%2BTc72j995rpfnzz/99LXZ%2BrQf/F/4KtGWL/nH7au2t0f7iCiums3%2BO871fBb2vOe3s67P/fHLOz9cK%2BkxkZXk/L3k/R5mZ3paR4W3Z2WX3HklS%2BfJSlSom2OW0Sy4xu2bktFq1vC0kpGzrAeBFAATOcvKkuWV19Khpx497ezaSk01vR2qqaXl7Q9LSzPfmtNOnzT/EuJARhYY/SRqphAu%2Bys9zD2iOfBMAA5HLZSZNhIZKYWHeVrGiaeHhpkVEmGNkpGlRUaa53aZVqiRVrmxaeDg9doC/IgDCEdLTzQDzAwfMYPLffzfjjg4eNO3QITM%2B6cgRE9zKQvny5h/XChVMT0eFCt5Wvry3BQeblnMeFOQ9BgWZdcvOPi9XzjSXK/95znXOed5ryftYznneY2HneZX0H/hx48aqX79%2Batbs8vM%2Bp8G2etKswl%2Bnx/CGalmrZLWcrSg/W1Her/O9z%2BdrZ/9Z5r3O%2B%2BcdHOx9LO9nJOczlPM5Orvl/QyGhJgWHExYA5yEMYAICFlZZrbgzp3Srl1mO6e9e72zCM%2B3TMT5BAebW1dVqpgejUqVvD0cOT0eOT0gOT0i4eHe3pKKFU3vSU6PSmio%2BYca5yrSGMCsLDMIbN8%2BSQWMAWzbVlqzpuyLBYAAQQ8gbCUz06z/tWmTWfh161azDtjOnfknhxYkNFSqU0eqXdsca9Y0rUYNqXr1/OOUIiPpDfErQUHSJ5%2BYTVlTU/N/rXp1aepUS8oCALsiAMJvZWSY1fx//tls57R%2BvQl95wt6ISGmk%2Biyy8yxYUPvLMK6dU1vHqHOP5w4cUI7d%2B7Mvd6zZ4/i4%2BNVpUoV1a1bt%2BBvuuoq84GIi5O%2B%2B87M%2Bn36aWnUKJPkAQBFxi1g%2BI2kJLM5%2B4oV0o8/msBX0NpgERHSH/5gWkyMWQesWTOzbAQr/NvD0qVL1bVr13MeHzRokKYWoTcvJSVFbrdbycnJioqKKoMKASCwEQBhmbQ0adkyadEi6fvvTefO2apUkWJjzVZObdpIV1wh1a/PeDqnIwACQMlwCxg%2B9dtv0ldfmU0dli07t4fv8sulLl3M3b6OHc2K/9y2BQCgdBEAUeZ27pRmz5Y%2B%2B8xs5ZpXvXpS9%2B5St25S165mPD8AAChbBECUiSNHpJkzpenTpbVrvY%2BXKyd17izdfLPUq5cZu0cPHwAAvkUARKnJzjZj%2BSZOlL780rsXaFCQdN110p13Sr17m62gAACAdQiAKLHUVGnKFGnCBGnHDu/jrVtLgwZJ/fpxaxcAAH9CAMRFS0yU3npLevdds0euZHbIGDBAeuABqWVLa%2BsDAAAFIwCi2BISpH/8Q3r/fe%2B%2BuU2bSn/%2Bswl/ERHW1ofAFRcXp7i4OGVlZVldCgDYGusAosiOHzfB7623pFOnzGPt20ujR5tJHazNB19hHUAAKBl6AHFBZ86Y3r6xY81uHZLUoYP04otm%2BRZm8QIAYC8EQBRq5UrpwQe9u3TExEh//7t0000EPwAA7IqbdihQSoo0YoRZs2/jRrMlW1yc9J//mNu9hD8AAOyLHkCcY/FiacgQad8%2Bcz10qBn7V62atXUBAIDSQQ8gcmVmSk8/bcb17dsnNWhgwuCHHxL%2BAAAIJPQAQpK0f7/Ut6%2B0apW5vv9%2B6fXXWdIFAIBARACEli%2BX%2BvSRDh%2BW3G6zq8dtt1ldFQAAKCvcAna4KVPMLd/Dh6UrrpDWryf8AQAQ6AiADuXxmHX8hg41Y//69jVLvjRsaHVlAACgrBEAHSg722zbNnasuX7mGWnmTKliRWvrAgAAvsEYQIfJzpb%2B9Cdp0iSzlt%2BECWahZ8AO2AsYAEoHewE7iMcjjRwpvfuu2bd36lRpwACrqwKKj72AAaBk6AF0kOeeM%2BHP5ZKmT5f697e6IgAAYAXGADrE%2B%2B9LL79szt97j/AHAICTEQAdYNEic%2BtXkl54QXrgAWvrAQAA1iIABri9e6W77pKyssx4v%2Beft7oiAABgNQJgAMvIkO68Uzp6VIqNlSZONOP/AACAsxEAA9izz0pr10pVqkiffSaFhlpdEQAA8AcEwAC1fLn02mvmfPJkqW5da%2BsBAAD%2BgwAYgE6dMlu8eTzSsGFS795WVwQAAPwJATAA/fWv0q5dUp060uuvW10NAADwNwTAALNzp/Tqq%2Bb87bclNkkAAABnIwAGmKefNrN/r79euvVWq6sBSldcXJxiYmIUGxtrdSkAYGvsBRxAVq%2BWOnQw%2B/xu3Cg1b251RUDZYC9gACgZegADyNix5jhwIOEPAACcHwEwQKxfL82fLwUFsdsHAAAoHAEwQORM/OjXT2rY0NpaAACAfyMABoDEROnTT835Y49ZWwsAAPB/BMAAMGWKdOaM1LGj1Lq11dUAAAB/RwC0OY9HmjrVnN9/v6WlAAAAmyAA2tz69dL27VJYmNSnj9XVAAAAOyAA2txnn5njTTdJkZHW1gIAAOyBAGhzX31ljrfdZm0dAADAPgiANrZ/v7R1q9n544YbrK4GAADYBQHQxhYvNsfYWKlyZWtrAXyBvYABoHQQAG1sxQpzvOYaS8sAfGbkyJHaunWr1qxZY3UpAGBrBEAbW73aHDt2tLYOAABgLwRAmzp9WvrlF3Petq21tQAAAHshANrUtm1SVpZUpYpUu7bV1QAAADshANrUtm3mePnlkstlbS0AAMBeCIA2tWePOV52mbV1AAAA%2ByEA2tSBA%2BYYHW1tHQAAwH4IgHazb580cqTGf1BNpxSqEbOulubMsboqAABgIy6Px%2BOxuggU0Y4dUufO0qFD537txRel55/3fU2ABVJSUuR2u5WcnKyoqCirywEA26EH0E6eeKLg8CdJ48Z5BwYCAAAUItjqAuzM4/EoNTXVN/%2BxpCRp3rzzfz07W5o4URo92jf1AD6Unp6u9PT03Oucv3cpKSlWlQQgAERGRsrl0KU0uAVcAjm3oQAAgP04eRgJAbAELqYHMDY29uL2MT1xQmrUSDp16vzPefllaeTI4r92Serywev542ulpKQoOjpa%2B/fvL5X/efjjz1gWr3exr3V2D2BCQoLatWunrVu3qk6dOpbVZafXKs3Xc8rnn/c/8F/LyT2A3AIuAZfLVey/fEFBQRf3FzYqSrr3XmnSpIK/XrGiNHy4ed5FuOi6fPB6/vpakhQVFVUqr%2BfPP6M/1xYZGRnQ778//1lKgf/55/3ntQIZk0B8bORF9tBJkv75T6lNm3Mfr1BB%2Bvhjsy%2BcFXWV8ev562uVJn/%2BGf25ttLirz%2BjP/9ZliZ/fc94/3mtQMYtYLs5fVqaOVPxf5mptENpKn9Va7Wb8ojUuLHVlTkKy5BY68CBA7m3wC699FKry3EcPv/W4v1HaaAH0G5CQ6UhQ/Rh32/UWSs1u8MbhD8LhISEaOzYsQoJCbG6FEfKed95/63B599avP8oDfQA2tRrr5llAe%2B6S5o1y%2BpqAN%2BiBwQASoYeQJu67DJz3LnT2joAAID9EABtqlkzc9y2zawBDQAAUFQEQJtq1MgMB0xLoxcQAAAUDwHQpoKDpVatzPnatdbWAgAA7IUAaFNz5sxRQsJcSVL//hMUHx9vcUWBx%2BPxaNy4capdu7bCwsJ0zTXXaMuWLYV%2Bz7hx4%2BRyufK1mjVr%2BqhioPS88847atCggUJDQ9WmTRutWLHivM%2BdOnXqOZ97l8ul06dP%2B7DiwLd8%2BXLdfPPNql27tlwul7744gurS4KNEQBtKi0tTR07Zv7vqqultQSqf/7zn3r99dc1YcIErVmzRjVr1lT37t0vuP1f8%2BbNlZCQkNs2bdrko4qB0vHvf/9bo0aN0rPPPqsNGzaoS5cu6tmzp/bt23fe74mKisr3uU9ISFBoaKgPqw58aWlpatWqlSZMmGB1KQgAbAVnUwMGDFCvXtKnn3qUnd1ciYmF90yheDwej9588009%2B%2Byzuv322yVJ06ZNU40aNfTJJ59o%2BPDh5/3e4OBgev3KSFxcnOLi4pSVlWV1KQHt9ddf17Bhw3TfffdJkt58803Nnz9f7777rsaPH1/g99DbXfZ69uypnj17Wl0GAgQ9gDZWpYp0xRXpkqQVK1gLrTTt2bNHiYmJ6tGjR%2B5jISEh%2BuMf/6gff/yx0O/dsWOHateurQYNGqhfv37avXt3WZfrGCNHjtTWrVtLbYN4nCsjI0Pr1q3L99mXpB49ehT62T9x4oTq1aunSy%2B9VDfddJM2bNhQ1qUCKAECoM11735SkrRoUSWLKwksiYmJkqQaNWrke7xGjRq5XytI%2B/btNX36dM2fP1%2BTJk1SYmKiOnXqpKSkpDKtFygtR44cUVZWVrE%2B%2B82aNdPUqVP11VdfaebMmQoNDdVVV12lHTt2%2BKJkABeBAGgDM2bMUERERG7LOxj7xhtNAFy7NkIJCVZVaH9nv8eZmWZ8pcvlyvc8j8dzzmN59ezZU3fccYdatGihbt266f/%2B7/8kmdvHgJ0U57PfoUMH3XvvvWrVqpW6dOmi2bNnq0mTJnr77bd9USqAi8AYQBu45ZZb1L59%2B9zrOnXq5J7XrXtG0k/Kzu6ojz%2BWnnzSggIDwNnvcXq6ubWemJioWrVq5T5%2B6NChc3pGChMeHq4WLVrQEwLbqFatmoKCgs7p7SvOZ79cuXKKjY3lcw/4MXoAbSAyMlKNGjXKbWFhYWc9Y7IkadIkdgW5WGe/xzExMapZs6YWLlyY%2B5yMjAwtW7ZMnTp1KvLrpqen65dffskXIgF/VqFCBbVp0ybfZ1%2BSFi5cWOTPvsfjUXx8PJ97wI8RAG3q6NGjio%2BP19atWyXNVFhYhnbskGbNOmp1aQHB5XJp1KhRevnllzV37lxt3rxZgwcPVsWKFXXPPffkPu%2B6667LtyTDE088oWXLlmnPnj1avXq1%2BvTpo5SUFA0aNMiKHwO4KI899pg%2B%2BOADTZ48Wb/88oseffRR7du3T3/6058kSQMHDtTo0aNzn//CCy9o/vz52r17t%2BLj4zVs2DDFx8fnPh%2Bl48SJE4qPj89d93XPnj2Kj48vdHke4Hy4BWxTX331lYYMGZJ7fepUnKRH9dxzx3XPPVWsKyyAPPXUUzp16pQefPBBHTt2TO3bt9eCBQsUGRmZ%2B5xdu3bpyJEjudcHDhzQ3XffrSNHjuiSSy5Rhw4dtGrVKtWrV8%2BKHwG4KHfddZeSkpL04osvKiEhQX/4wx/0zTff5H6O9%2B3bp3LlvP0Hx48f1wMPPKDExES53W5deeWVWr58udq1a2fVjxCQ1q5dq65dveu%2BPvbYY5KkQYMGaerUqRZVBbtyeTwej9VFoOR%2B%2B83sD3zmjLRqlZRnOBsQcFJSUuR2u5WcnKyoKJZAAoDi4hZwgKhXT7r3XnM%2BZoy1tQAAAP9GAAwgzz8vlS8vLVggLVpkdTUAAMBfEQADSMOG0ogR5vzRR83tYAAAgLMRAAPMmDFmi7jNmyXWYEWgiYuLU0xMjGJjY60uBQBsjUkgAWjSJOm2oh48AAAXJElEQVSBB6TwcGnLFjM%2BEAgkTAIBgJKhBzAADRsmdekipaVJQ4awODQAAMiPABiAypWTJk%2BWKlaUliyRXn/d6ooAAIA/IQAGqEaNpDfeMOejR5u1AQEAACQCYEC7/37pzjvNbOA775QOHrS6IgAA4A8IgAHM5ZI%2B%2BEBq2lQ6cEC67Tbp9GmrqwIAAFYjAAa4qCjpq6%2BkSpWkn36SBg1iUggAAE5HAHSAJk2kOXPMLiGzZ0sPPyyx%2BA8AAM5FAHSIrl2l6dPNbeF33pGeeooQCACAUxEAHaRfP%2Bm998z5q69Kjz9OCAQAwIkIgA7zwAOmB1Ayy8Tcdx97BgMA4DQEQAcaMcIsFJ2zYHTv3lJqqtVVARfGXsAAUDrYC9jBvvzS3BY%2BfVpq2dJc169vdVXAhbEXMACUDD2ADta7t7R0qVSjhrRxo9S2rbRggdVVAQCAskYAdLj27aU1a0z4S0qSbrhBevZZKTPT6soAAEBZIQBC0dHSihXS8OFmVvDLL0udO0u//mp1ZQAAoCwQACFJCg01S8T8%2B9%2BS2y39/LN0xRVmuRhmCQMAEFgIgMinb19p0yapWzczOeTJJ81t4tWrra4MAACUFgIgzhEdbSaDTJpk9hBev17q0EEaOlRKSLC6OgAAUFIEQBTI5TKLRG/bJg0caB6bMkVq3FgaO1ZKSbG2PgAAcPEIgChUjRrStGnSjz%2BaXsC0NOnFF6WGDaW//50FpAEAsCMCIIqkY0cTAj/9VGra1CwZM3q0VK%2BeNGaMdOiQ1RUCAICiIgCiyFwuqU8fafNmafp0EwSPHZNeekmqW1caNkzasMHqKgEAwIUQAFFswcHSgAHSli2mR7BdOyk93ewr3Lq1uVU8ebJ04oTVlSLQsBcwAJQO9gJGiXk80k8/SW%2B/LX32mXfdwPBw6Y47pP79pWuvNcERKA3sBQwAJUMARKk6eNDMFp48Wdqxw/v4JZdIt99uAuE110jly1tWIgIAARAASoYAiDKR0yv48cfS7Nlm0kgOt9vsOdyrl3T99VL16tbVCXsiAAJAyRAAUeYyM6XFi6XPP5e%2B%2BEI6fDj/16%2B8UrruOqlrV6lLFyky0po6YR8EQAAoGQIgfCory%2BwzPG%2Be9M03Unx8/q%2BXK2f2IO7c2Sw9066d1KCBmYEM5CAAAkDJEABhqcRE0zv4/ffSkiXSnj3nPqdaNalNGzPD%2BIorpFatpEaNpKAg39cL/0AABICSIQDCrxw4IK1cadqqVaaHMDPz3OeFhkrNmknNm5tjs2ZSkybSZZeZ2ccIbClHj8pdtSoBEAAuEgEQfu30aWnjRmndOrPIdHy8WYj61Knzf0/NmiYINmgg1a9vFqmOjjatTh0zCYVbytaZM2eO3n//fa1bt05JSUnasGGDrrjiiqJ98/z50t//rpSlS%2BWWlNy9u6LGjZM6dSrLkgEg4BAAYTtZWeZW8ZYt0i%2B/mPbrr2bZmaNHL/z94eFSrVqm1ahhWvXqZqmaatVMq1pVqlJFqlxZqliRwFiaPvroI%2B3Zs0e1a9fW/fffX/QAOHWqNHSo5PEoRTIBUFJU%2BfLS3LlmWjkAoEgIgAgoR49Ku3aZgLhnj7R3r7Rvn7R/v2nHjxf/NYODpUqVTM9hTouMlKKipIgIb6tY0bTwcCkszNtCQ70tJMS0ChXyt/LlzQQYJ9m7d68aNGhQtAB48qRUu7aUnCxJ%2BQOgZLp7d%2B503psIABeJAAhHSUuTfv9dSkgwE1AOHjTt0CGzPM2RI6YlJZl9jnN2NfGFcuVMECxf3oTO8uXNRJegIHOdc57TypXLf3S5zHlBx5yW91o697ygY2HneRW3l/TkyZP6/vtFuvrqP8rtdhf63M4HPtFTG/rnXp8TACWN7rRMW6peXbwiiqmwn/FC79f53vOCWt4/v/O1sz8HOZ%2BRvMezP0/ly5/7y0fOLyUhIeaXlJxfWsLCzC805cvTAw4EIjbngqOEh0uNG5t2IR6PCYzHjpmew%2BRk01JSpNRUb0tLM/sep6WZjqq0NDNG8dQpM4Yx55ienr%2Bd/atXdrb3a85QUdItWr78ws%2Bsqwvf2//1xyP6uuRF4SxBQd7e7YgI83cop9c7pyfc7fYeK1c2PeY5QyhyhlOEhVn9kwDIiwAInIfL5f2HLjq6dF/b4zFjGTMyTMvMNO3MmfznWVneY0EtO9u0nHOPJ/8xO9v738v7tZzwefZ53mNh54U9lteqVas0ffr03OtRo0apSZMmkqQjR47oL395WmPGjFXdunULfZ1LNjeV3iz8v3XPSzHqVavw51xIce%2BHnP38gt6vgo5nn%2Bdtef%2BM8v4Z5/0zzXm8oM/EmTP529mfq5zPXEaG9xeOjAzzS0pOO3XKW2NWlveXnYMHi/f%2B5BUebsbZXnKJGXObM/62Vi1zdz9vY6tIoOxxCxhAmUlNTdXBPKmhTp06CvtfV1CxxgB6PGbNn19%2BkVTALeBrrzWLSaJUeDwmFJ46ZXq1c3q2c3q7T5zI3wue0zuenGx6y48dM%2B3oUdOysor%2B33a5zEz%2BunVNq1/fDPFs2NC0%2BvUJiEBpoAcQQJmJjIxUZGns7edySZ9%2BKnXvbgZw5tWokZkhjFLjcnnHBVaqVLLX8nhMMMwZX5sz5vbQITMONzHRjMv9/Xfpv/81vZQJCaatXn3u6wUFmRDYpInUtKlpMTGmVatWsloBJ6EHEIBPHT16VPv27dPvv/%2BuXr16adasWWratKlq1qypmjVrFv7Nx49L06Yp5Ztv5F6wQMlvvaWo%2B%2B4zA9Rge9nZZjLW/v1m9v5vv5m2e7e3FbYGaI0a0h/%2BILVsaXYNuvJK6fLLzSQYAPkRAAH41NSpUzVkyJBzHh87dqzGjRtXpNdgKzhn8nhMz%2BD27aZt22baL7%2BYJZ8KEhpqAmHbtmZv8Q4dzCQwVgyC0xEAAdgOARBnO3FC2rpV2rTJ7B4UH29aSsq5z61cWerYUbrqKqlLFyk21gRFwEkIgABshwCIosjONgvDr1sn/fyzaevXn3sbOSTEBMKuXaXrrjM9hUw0QaAjAAKwHQIgLlZmpvSf/0grV0o//CCtWHHu8jaRkSYMXn%2B91LOnmYUMBBoCIADbIQCitHg8ZjzhkiXS4sWmJSXlf87ll0s33STdfLPUqZOZiQzYHQEQgO0QAFFWsrPN2MH586XvvjM9hXnXMaxWTerdW7r9dqlbN7OdHmBHBEAAtkMAhK8cP27C4Lx5ph0/7v1apUrSrbdK/fqZsYMsNwM7IQACsB0CIKyQmSktXy7NmWNaYqL3a9WrS3fdJQ0cKLVpYxbTBvwZARCAbcTFxSkuLk5ZWVnavn07ARCWycoyk0j%2B/W%2BzSc2RI96vxcRIgwebMFijhmUlAoUiAAKwHXoA4U8yM6WFC6WPPpK%2B%2BEI6fdo8HhxsJo4MH252MWTxafgTAiAA2yEAwl8lJ0uzZ0sffph/L%2BOGDaU//UkaOlSqWtW6%2BoAcBEAAtkMAhB1s3ixNmiRNm2aCoWR2HLn3XunPfzb7FgNWIQACsB0CIOzk5Elp1ixpwgRpwwbv4927S48/LvXowaQR%2BB4jEgAAKEMVK5pbv%2BvWmZ1H%2BvQx4wEXLpRuuEG64grpk0%2BkM2esrhROQgAEAMAHXC6pc2cza3jXLnMbODxc2rhR6t9fatrU3DLOyLC6UjgBt4AB2A63gBEojh2T4uKkt97yLiUTHS0984w0ZIgUEmJtfQhc9AACAGCRypWl556T9u6VXn9dqlVL2r9fGjFCatJE%2BuADbg2jbBAAAQCwWHi49Oij0u7d0ttvS7VrS/v2SfffbxaWnjXL7FMMlBYCIAAAfiI0VHroIWnnTtMjeMkl0o4d0t13S7Gx0vffW10hAgUBEAAAPxMWZnoEd%2B2SXnhBioyU1q%2BXunWTbrxR2rrV6gphdwRAALYRFxenmJgYxcbGWl0K4BORkdKYMSYIPvyw2V7u22%2Blli2lkSOlpCSrK4RdMQsYgO0wCxhOtWOH9NRTZs9hyUwieekls99wcLC1tcFe6AEEAMAmGjeW5s6VFi82vYDHjpkxg23bSitXWl0d7IQACACAzXTtanYWiYszvYD/%2BY9ZZHrYMO96gkBhCIAAANhQcLD04IPS9u0m%2BEnS5MlSs2bS1KkSA7xQGAIgAAA2Vq2aWTB65UqpRQszMWTIEDNjeOdOq6uDvyIAAgAQADp1MreF//EPs57g4sUmEL72mpSVZXV18DcEQAAAAkT58maW8ObN0nXXSadPS088IV11lfTLL1ZXB39CAAQAIMBcdpm0cKE0aZIUFSWtXi1deaX06qv0BsIgAAIAEIBcLum%2B%2B6QtW6QbbpDS06UnnzQziPfssbo6WI0ACABAALv0Uumbb0xvYESEtGKFWUOQmcLORgAEACDA5fQGbtxo1gs8ccLMFO7bVzp61OrqYAUCIADbYC9goGQaNJCWLpVeftmsI/jZZ1KrVtLy5VZXBl9jL2AAtsNewEDJrV0r3XOP2V%2B4XDlpzBjpueekoCCrK4Mv0AMIAIADtW0rrV8vDRokZWdL48ZJ3btLCQlWVwZfIAACAOBQERFmMshHH0nh4dKSJWa5mMWLra4MZY0ACACAw917r9lFpEUL6eBB0xP4t7%2BZnkEEJgIgAABQ06ZmweihQ03we%2B45qXdv6fhxqytDWSAAAgAASVJYmPThh9IHH0ghIdK8eWas4KZNVleG0kYABAAA%2BQwbJv34o1SvnrRrl9Shg/Tpp1ZXhdJEAAQAAOdo3dqMC%2BzeXTp50iwa/eyzjAsMFARAAABQoKpVzTZyTzxhrl9%2BWbr1Viklxdq6UHIEQAAAcF7BwdIrr0gff2zGBX79tdSpk7Rnj9WVoSQIgAAA4IL695dWrJBq1ZK2bJHatZN%2B%2BMHqqnCxCIAAbIO9gAFrxcZKa9aY8YFHjkjXXSfNmGF1VbgY7AUMwHbYCxiwVlqaNHCgNGeOuR43zuwl7HJZWhaKgR5AAABQLOHhZlmYJ5801%2BPGSUOGSBkZlpaFYiAAAgCAYitXTvrnP6X33pOCgqRp06RevZghbBcEQAAAcNGGDzczg8PDpUWLpKuvlhISrK4KF0IABAAAJdKzp7RsmVSjhvSf/5hlYn791eqqUBgCIAAAKLE2bcz2cY0bS3v3Sp07mxnD8E8EQAAAUCoaNpRWrpTatjXLxHTtKi1caHVVKAgBEIDPZGZm6umnn1aLFi0UHh6u2rVra%2BDAgfr999%2BtLg1AKbnkEmnJErOHcFqamRjy2WdWV4WzEQAB%2BMzJkye1fv16Pf/881q/fr3mzJmj7du365ZbbrG6NAClKCLCTAzp00fKzJTuukuaPNnqqpAXC0EDsNSaNWvUrl07/fbbb6pbt26RvoeFoAF7yMqSRoyQJk0y12%2B%2BKf35z9bWBCPY6gIAOFtycrJcLpcqVap03uekp6crPT099zqFhcYAWwgKkt5/X3K7pVdflUaNklJTpeees7oycAsYgGVOnz6tv/zlL7rnnnsK7ckbP3683G53bouOjvZhlQBKwuUyC0a/8IK5fv556ZlnJO4/WosACKDMzJgxQxEREbltxYoVuV/LzMxUv379lJ2drXfeeafQ1xk9erSSk5Nz2/79%2B8u6dAClyOUyewW/%2Bqq5Hj9eeuIJQqCVGAMIoMykpqbq4MGDudd16tRRWFiYMjMz1bdvX%2B3evVuLFy9W1apVi/W6jAEE7CsuTnroIXP%2B8MPSW2%2BZgAjfYgwggDITGRmpyMjIfI/lhL8dO3ZoyZIlxQ5/AOxt5EipQgWzhdzbb0tnzphQSAj0LQIgAJ85c%2BaM%2BvTpo/Xr12vevHnKyspSYmKiJKlKlSqqUKGCxRUC8IX775fKl5eGDpXefdfcCo6Lk8oxMM1nuAUMwGf27t2rBg0aFPi1JUuW6JprrinS63ALGAgM06dLgwebADhiBD2BvkQPIACfqV%2B/vvidE0COgQPNcfBg0xMYFCT961%2BEQF%2BgsxUAAFhm4ECzS4jLJU2YID3%2BOLODfYEACAAALDV4sDRxojl/4w3WCfQFAiAAALDcffdJOUuC/v3v0ksvWVtPoCMAAgAAvzBihPT66%2BZ87FjptdesrSeQEQABAIDfePRR6a9/NedPPGH2EkbpIwACsI24uDjFxMQoNjbW6lIAlKFnnpGeftqcjxghzZxpbT2BiHUAAdgO6wACgc/jMVvGvfOOFBwsffGF1KuX1VUFDnoAAQCA33G5zFZx/fub7eL69JFWrLC6qsBBAAQAAH6pXDlpyhTpppuk06fNMT7e6qoCAwEQAAD4rfLlpdmzpS5dpJQU6YYbpF27rK7K/giAAADAr4WFSV99JbVqJR08KF1/vTni4hEAAQCA36tUSfr2W6lBA9MDeOONUmqq1VXZFwEQAADYQq1a0vz5UrVq0vr10h13SBkZVldlTwRAAABgG40bS998I1WsKC1caLaQY0G74iMAAgAAW4mNlT77TAoKkj76SHr%2Beasrsh8CIAAAsJ2ePaWJE8353/7mPUfREAABAIAtDR0qjR1rzh980EwSQdEQAAEAgG2NHSsNGiRlZUl9%2B7JQdFERAAHYRlxcnGJiYhQbG2t1KQD8hMtlbv9ee6104oTZLeTYMaur8n8uj4e5MwDsJSUlRW63W8nJyYqKirK6HAB%2B4Phxs1vI0KHSqFEmGOL8gq0uAAAAoKQqVZLWrpVCQqyuxB64BQwAAAIC4a/oCIAAAAAOQwAEAABwGAIgAACAwxAAAQAAHIYACAAA4DAEQAAAAIchAAIAADgMARAAAMBhCIAAbIO9gAGgdLAXMADbYS9gACgZegABAAAchgAIAADgMARAAAAAhyEAAgAAOAwBEAAAwGEIgAAAAA5DAAQAAHAYAiAAAIDDEAABAAAchgAIAADgMARAALbBXsAAUDrYCxiA7bAXMACUDD2AAAAADkMABAAAcBgCIAAAgMMQAAEAAByGAAgAAOAwBEAAAACHIQAC8Klx48apWbNmCg8PV%2BXKldWtWzetXr3a6rIAwFEIgAB8qkmTJpowYYI2bdqkH374QfXr11ePHj10%2BPBhq0sDAMdgIWgAlspZ1HnRokW67rrrivU9LAQNABcn2OoCADhXRkaGJk6cKLfbrVatWp33eenp6UpPT8%2B9TklJ8UV5ABCwuAUMwOfmzZuniIgIhYaG6o033tDChQtVrVq18z5//PjxcrvduS06OtqH1QJA4OEWMIAyM2PGDA0fPjz3%2Bttvv1WXLl2UlpamhIQEHTlyRJMmTdLixYu1evVqVa9evcDXKagHMDo6mlvAAHCRCIAAykxqaqoOHjyYe12nTh2FhYWd87zGjRtr6NChGj16dJFelzGAAFAyjAEEUGYiIyMVGRl5wed5PJ58PXxFed3k5OQivTYA4FwEQAA%2Bk5aWpr/97W%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%3D'}