Sentence examples for sensitive to discrepancies from inspiring English sources

The problem of audiovisual coherence is quite important: human interlocutors are very sensitive to discrepancies between the visible and audible consequences of articulation [10, 11] and have expectations on resulting audiovisual traces of the same underlying articulation.

We chose to use the CC to evaluate the trained models because it measures whether the 'shape' of the model predictions matches the 'shape' of the 'correct' transcriptional output, and compared with the RMS error metric, CC is less sensitive to discrepancies in the magnitude of the predicted output.

Students are sensitive to the discrepancies between harassment policies, the 'right words' intoned by Professors or administrators, and visible actions.

In those few cases, the results were not sensitive to the discrepancies with the underlying assumptions.

These differences with our findings are probably because laypeople are not as sensitive to such minor discrepancies as orthodontists, as shown by Machado et al. [19].

RH likelihood is sensitive to large scale ordering discrepancies, as produced by the assembly errors, but has difficulties to order closely related markers reliably.

In summary, systematic discrepancies are considerable and critical to include, and predictions are very sensitive to assumptions about the discrepancy term.

Although the presence of discrepancies seems sensitive to serious problems within trial reports, it is not specific, in that there are many trial reports in good standing with discrepancies.

Inverse techniques based on vibration tests and numerical calculations are sensitive to model errors, which bring discrepancies in the results of the estimated parameters.

In our study most of the strains are fully sensitive to nearly all antibiotics, therefore discrepancies are likely to be very rare and thus the rate of methicillin resistance presented in our study is likely to be very close to the reality.

However, the discrepancy function may be overly sensitive to small differences in fit between the model and the data.