Input shape and Conv1d in Keras

The first layer of my neural network is like this:

model.add(Conv1D(filters=40,
                 kernel_size=25,
                 input_shape=x_train.shape[1:],
                 activation='relu',
                 kernel_regularizer=regularizers.l2(5e-6),
                 strides=1))

if my input shape is (600,10)

i get (None, 576, 40) as output shape

if my input shape is (6000,1)

i get (None, 5976, 40) as output shape

so my question is what exactly is happening here? is the first example simply ignoring 90% of the input?