Sentence examples for discrepancy of the data from inspiring English sources

It was reported that there was discrepancy of the data on the forest area before and after 2000, since the data after 2000 lacks ground truth surveys and deviates significantly from earlier figures [23].

Zhang [23] develop a kernel mean matching (KMM) method to minimize the discrepancy of the data chunks in the kernel space for smooth concept drift and an Optimal Weight values for classifiers trained from the most recent data chunk for abrupt concept drift.

The discrepancy of the data from different human lupus miRNA studies may be due to the difference in the type of samples, sensitivity of detection methods, and the diversity in the medical history, disease severity and race of the lupus patients.

It was reported that there was discrepancy of the data on the forest area before and after 2000, since the data after 2000 lacks ground truth surveys and deviates significantly from earlier figures [ 23].

The major concern of additive experiments has been the ill-explained discrepancies of the data obtained.

However, definitive resolution of discrepancies of the data from us and Cooke et al with those of Maier et al must await further analyses (note: all 3 studies have been carried out in the 3D7 strain).

The discrepancy to the data of the aforementioned studies might be explained by differences in the anatomic localization, impregnation method and time, antibiotic, used, or amount of the drainage fluid over the early postoperative period.

Despite CA-AKI being a well-known complication outside the ICU, the paucity and the discrepancy of data in the ICU justifies additional data.

In all cases in which there was a discrepancy of data, the issue was discussed and resolved among the authors, adhering to the wording of the original reports as closely as possible.

They also had technicians fill out monthly questionnaires about their work and did statistical analyses for patterns of discrepancy in the data.

One common approach to anomaly detection makes use of unsupervised learning: we learn a baseline model of the phenomenon of interest, and then measure the discrepancy of subsequent data from the baseline.