What is the difference between 'features' and 'descriptors' in computer vision / machine learning?

I've read multiple time sentences similar to

Finally, for standard image classification bag-of-words features based on SIFT descriptors have been found critical for high performances. We first compute a standard SIFT discriptor at regular grid points over the whole image.

Source: "Multi-class image segmentation using Conditional Random Fields and Global Classification" by Nils Plath, Marc Toussaint, Shinichi Nakajima.

What is a descriptor? I thought SIFT is an algorithm which operates on images and gives features (vectors in $\mathbb{R}^n$, where $n$ is fixed for a fixed size of images and parameters of the SIFT algorithm)?

SIFT works by analyzing the image, identifying a set of keypoints (a set of points in the image that will be helpful for alignment), and then for each keypoint, it computes a descriptor vector (a feature vector). Then it uses the descriptor vectors for the keypoints in image $I_1$ and the descriptor vectors for the keypoints in image $I_2$ to try to align the two images to each other. The intuition is that if the descriptor vector for a keypoint in image $I_1$ is "similar" to a descriptor vector for a keypoint in image $I_2$, then maybe those two points should be aligned to each other. Here "similarity" is measured by the Euclidean distance between the two descriptor vectors.
Thus, a descriptor vector for a keypoint is a vector, e.g., in $\mathbb{R}^{128}$, chosen so that if the image is translated, scaled, rotated, etc., then the descriptor vector for that point won't be changed much by the transformation.