diff --git a/app/Main.hs b/app/Main.hs
index e342b4b..b6fbc3e 100644
--- a/app/Main.hs
+++ b/app/Main.hs
@@ -104,11 +104,30 @@ loop loopAmount ps env = loop' loopAmount 0 ps env
           (!fs,cs) <- unzip <$> fitness plants
           let fps = zip plants fs -- gives us plants & their fitness in a tuple
               sumFitness = sum fs
-              spc = meanAndVar `from` sumProducedCompounds $ cs
-              ndc = meanAndVar `from` numDistinctCompounds $ cs
+              -- $C_{\Sigma,mu}$: Durchschnittliche Menge an produzierten Stoffen
+              -- $C_{\Sigma,sigma}$: Durchschnittliche Varianz an produzierten Stoffen
+              (c_Sigma_mu, c_Sigma_sigma) = meanAndVar `from` sumProducedCompounds $ cs
+              -- - $C_{i,\mu}$: Durchschnittliche Anzahl produzierter Komponenten
+              -- - $C_{i,\sigma}$: Zusätzlich: Betrachtung der Varianz dieser Komponenten innerhalb der Population  
+              --   (Z.B. Stoff A wird immer mit $0.5$ produziert, hat also keine Varianz,
+              --   wogegen Stoff B *im Schnitt* mit $0.5$ produziert wird, aber dies eine extreme
+              --   Varianz auslöst)
+              --   c_mu_sigma :: [(Amount,Amount)]
+              (c_i_mu,c_i_sigma) = unzip $ meanAndVar `from` id <$> byProducts cs
+              -- - $C_d$: Durchschnittliche Anzahl distinkter Produzierter Stoffe (sprich
+              --   nicht-endemisch, $#i | C_{i,\sigma} > \epsilon$ )
+              isEndemic :: Vector Bool
+              isEndemic = (> 0.01) <$> fromList c_i_sigma
+              (c_d_mu, c_d_sigma) = meanAndVar `from` (countWith isEndemic) $ cs
+              -- - $C_{\sigma,\{\mu/\sigma\}}$: Mittelwert/Varianz von $\C_{i,\sigma}$
+              (c_sigma_mu, c_sigma_sigma) = meanAndVar `from` id $ c_i_sigma
+              -- - $\mathbf{E}[C_{\Sigma,plant} - C_{\Sigma,mu}]$: Durchschnittliche Abweichung der produzierten
+              --   Stoffe gegenüber dem Schnitt der Gesamtpopulation
+              e_hash_plant = F.mean `from` numDistinctCompounds $ cs
+              -- mean and variance of fitness
               fns = meanAndVar `from` id $ fs
-              cstats = meanAndVar `from` id <$> byProducts cs
-              cstats' = meanAndVar `from` id $ snd <$> cstats
+              -- - $P_\{\mu,\sigma\}$ Mittelwert/Varianz der Anteile der Stoffe in Pflanze i, die giftig sind
+              toxs = meanAndVar `from` percentagePoisonous $ cs
           when (curLoop `mod` printEvery == 0) $ liftIO $ do
             printPopulation stringe (zip3 plants fs cs)
             putStrLn $ "Population statistics           (mean,variance):"
diff --git a/src/Evaluation.hs b/src/Evaluation.hs
index 295e60d..8bf0d42 100644
--- a/src/Evaluation.hs
+++ b/src/Evaluation.hs
@@ -1,16 +1,21 @@
+{-# LANGUAGE GADTs #-}
 module Evaluation
   ( sumProducedCompounds
   , numDistinctCompounds
+  , percentagePoisonous
   , sumCompounds
   , from
   , meanAndVar
+  , sumWith
+  , countWith
   , byCompound
   , byProducts
   ) where
 
 import Control.Foldl as F
 import Numeric.LinearAlgebra as LA
-import Data.List
+import Numeric.LinearAlgebra.Devel as LA
+import Data.List as L
 
 import Environment
 
@@ -33,6 +38,24 @@ numDistinctCompounds :: Functor f => f (LA.Vector Amount) -> f Amount
 --numDistinctCompounds :: [LA.Vector Amount] -> [Amount]
 numDistinctCompounds comps = sumElements . LA.cmap (\x -> if abs x < eps then 0 else 1) <$> comps
 
+countWith :: Functor f => (LA.Vector Bool) -> f (LA.Vector Amount) -> f Amount
+countWith toSelect = fmap $ sumElements . LA.zipVectorWith (\selected _ -> if selected then 1 else 0) toSelect
+--                                      apply selection to set data to 1 or 0
+--                        sum up 1 or 0s
+--                 for all data
+
+sumWith :: Functor f => (LA.Vector Bool) -> f (LA.Vector Amount) -> f Amount
+sumWith toSelect = fmap $ sumElements . LA.zipVectorWith (\selected d -> if selected then d else 0) toSelect
+--                                      apply selection to set data to 1 or 0
+--                        sum up data or 0s
+--                 for all data
+
+percentagePoisonous :: Functor f => [Int] -> f (LA.Vector Amount) -> f Amount
+percentagePoisonous poisons = fmap percentage
+  where
+      percentage v = (\(tox,total) -> tox / total) $ foldl1' pfold [(if i `L.elem` poisons then v!i else 0, v!i) | i <- [1..LA.size v]]
+      pfold (a,b) (a',b') = (a+a', b+b')
+
 -- | helper function for Foldl-Package.
 --
 --   Usage: @F.mean `from` sumCompounds $ v@ where v is a Set/List/Vector/... of Vector of Compounds.