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We define precision of a cluster i which contains various first name initials indexed by j: P i = max j ( Frequency [ FirstNameInitial ( j, K i ) ] ) | K i |. (3).

We define precision by CP/ CP + IP).

We define precision = TP/P and recall = TP/T.

As previously mentioned,10% of the interviews are repeated over a 1 3 months interval after the first interview to define precision and stability over time.

As before, we consider a set of propagated experimental terms T and predicted terms and define precision as the fraction of terms predicted correctly.

For a given level in the taxonomy tree (e.g., genus), we define precision as the fraction of correct assignments over the total number of assignments, and sensitivity as the ratio between the number of correct assignments and the number of objects to be classified.

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Similar(51)

Moreover, we do not calculate the number of repetitions in order to achieve a defined precision, because the execution would be infeasible (conclusion validity).

They also defined Precision as "the probability that a predicted positive class instance is a true positive" and explained Recall as "the probability of success in recognizing a positive class instance".

We defined precision by CP/ CP + IP).

A second problem is that Kembel and Cahill (2005) defined precision as the percentage of total roots inside a patch which is not predicted from the model presented here.

For example, some authors defined precision as the mass of roots inside a patch as a fraction of total body mass (Campbell et al. 1991) but a model for this type of foraging precision would require significantly more complex treatments of root growth relative to nutrients and shoot growth relative to photosynthetically active radiation that are beyond the scope of this manuscript.

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