Sentence examples for familiarity score from inspiring English sources

Ms. Burnett's "viewer familiarity" score has hovered around 6percentt.

77% reported an improvement in their familiarity with clinical nephrology practice (median 2-point increase in familiarity score on a 7-point scale, P < 0.001 by signed rank testing).

We use (M^) to denote worker-landmark matrix of the accumulated familiarity score, where (m^_{ij}) equals to ( F_{w_i}^{l_j}).

For example, if a worker (w_1) has high familiarity score with (l_1, l_2), and ( l_3), and another worker (w_2) living nearby has high familiarity score with (l_1) and (l_2), (w_2) is also likely to be familiar with (l_3) though (w_2) has not answered any question relating to (l_3).

In this way, the worker with high accumulate familiarity score will get a relatively high preference score and ensure the preference score will not result in a bias in worker selecting.

As a result, the accumulated familiarity score (F_{w_i}^{l_j}) of (l_j) of a worker (w_i) is a weighted sum of all the landmarks in the (eta _mathrm{dis}) range of (l_j).

Because the number of consultations with a familiarity-score of 1 (not at all/hardly familiar) and 2 (moderately familiar) was smaller than category 3 (very familiar), consultations with familiarity scores 1 were chosen first, followed by consultations of the same GP scoring 2 and 3 on familiarity.

In our dataset, we separated the data from songs into a high familiarity data group (4 6 familiarity scores) and a low familiarity data group (1 3 familiarity scores).

However, simply adding up a worker's accumulated familiarity scores on all the landmarks of (mathbb {L}_{mathbb {R}}) may lead biased result in worker selection.

Therefore, we need to predict familiarity scores of workers on landmarks using the latent similarity between workers and that of landmarks.

Comparing the adding up sum of accumulated familiarity scores of the ten landmarks, (w_1) will be selected to be assigned the task.