[Python] When I use ss2tf on the python control package I get extra terms in the numerator

I'm doing some prework for a control design of a system, with the control package in python. I write the different state space matrices, and convert that system to transfer functions (ss2tf function in the python control system library).

I seem to be getting some weird values one some states. In the example below I get the response from input 1 to output 2 (H[1,2])to be

0.2991 s + 14.96
--------------------
s^2 + 50 s + 0.02381


When I manually do the calculations I get

0.2991
--------------------
s + 0.000476190476


It seems as the expressions are quite similar if I do some rounding and factor out the first expression. But I don't understand why I get higher order transfer functions when I do ss2tf.

See the code above. Appreciate the answers!

#Parameters defining the system
V = 20000 #[m^3 volume in tank]
K_qo = 20000/(35*60)
C = np.pi * 20.25**2
O2Sea = 7 #g/m^3


K = 104.2*10**3/(60**2)
K1 = 20000/((60**2) * 130)
K2 = 7 * K1
T=0.02

s= sy.symbols('s')

a0 =K_qo/(0.06*C)
a1 = K_qo/V
a2 = 1/T

b0 = K1/C
b1 = K2
b2 = K/T


A = np.array([[-a0, 0, 0,0,0],
[0, -a1, 1,1,1],
[0, 0, -a2 ,0, 0],
[0, 0, 0, -a2, 0],
[0, 0, 0, 0, -a2]
])

B = np.array([[b0, b0, b0, 0, 0,0,0],
[b1, b1, b1, 0, 0, 0, -1],
[0, 0, 0, b2, 0, 0, 0],
[0, 0, 0, 0, b2, 0, 0],
[0, 0, 0, 0, 0, b2, 0]
])

C = np.array([[0,0, 0,0,0],
[0,1, 0,0,0],
[0, 0, 0,0,0],
])

D = 0



ssmodel = ct.ss(A, B, C, D)
print(ssmodel)

H = ct.ss2tf(ssmodel)

print(H[1,2])

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