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Furthermore, we conducted an error propagation analysis for both scenarios and estimated uncertainties at pixel level using Monte Carlo simulations.
We also conducted an error evaluation of the thermal structure by comparing the maximum (+ 12.5%), average, and minimum (− 12.5%) basal heat flow (or temperature gradient, (alpha)) values (Fig. 9c).
In this study, we conducted an error evaluation of the heat flow values by varying the P-wave velocity between 1700 and 1900 m/s based on the relationship between the two-way travel time and the depth obtained from IODP Site C0002 (Expedition 314 Scientists 2009) (Table 1) to account for this significant variation in the velocity in the sediment with the presence of different gases or porosities.
At the end of each iteration of DeepDive, we conducted an error analysis to identify sources of error in the system.
For each iteration, we conducted an error analysis; we calculated precision and an estimated recall using 100 random candidate relations with a probability greater than 90% and 100 random candidate relations with a probability less than 90%.
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We also conduct an error analysis for the temporal discrete system and show that the error bound is independent of time.
First, we conducted an ANOVA on error rates for negative probes with gF, interference expectancy, and recency as factors (Table 2).
To assess whether performance was equivalent for superior and inferior targets (whether there was any evidence of altitudinal neglect), we conducted an analysis of error rate for targets above and below the horizontal meridian.
We conducted an exploratory analysis of HR error correction for 24 dogs, and also statistical comparisons between the Polar error correction and the AVEC.
Johnson and Green [ 16] compared p-values of items as well as conducted a qualitative examination of error types (e.g., transcription error, place value error, partial answer, computation error, misunderstanding) made by students in each mode.
After conducting a different error decomposition using the above concentration inequality, we provide an upper bound or learning rate of the expected generalization error.
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