Update README.md

README.md

@@ -57,7 +57,7 @@ Creating quantitative accuracy metrics for a system like this is not always easy
 
 To provide one indicative measure, we validated a random sample of 500 detections from 2023. This allows us to estimate what is known as the precision or positive predictive value for the classifier. In essence, it tells you the likelihood that a patch marked as a mine is actually a mine. 498 of 500 samples have artisanal mining scars. Of the 2 model errors, 1 is an industrial mine, and 1 is a remnant of construction of the Balbina dam and power station from around 1985. Together that lets us assert an estimated precision for the classifier of 99.6%. 
 
-Recall is harder, because we don't know what we might be missing. Our small labeled dataset does not give a sufficiently representative sample of the Amazon basin to derive reliable metrics.
+Recall is harder, because we don't know what we might be missing. Our small labeled dataset does not give a sufficiently representative sample of the Amazon basin to derive  metrics we trust.
 
 #### Area estimation