Thus, for the application of Eq. (13) to substantial earthquake data, it was necessary to check the extent to which the time-harmonic wave predictions explained the amplitude level variance for non-time-harmonic waves.
This significant reduction in the number of false-positive predictions explains the large increase in the sPPV values.
Researchers have published a study suggesting a theoretical prediction explaining the unusual brightness of some astronomical explosions, first developed by Columbia University astronomers and physicists in 2014, is correct.
The WQI predictions of this model had significant, positive, very high correlation (r = 0.977, p < 0.01) with the measured WQI values, implying that the model predictions explain around 95.4% of the variation in the measured WQI values.
The neural predictions explain about 76% of the variation in individual pitch percepts (Figure 3f, df = 1,108; F = 336.3, p<0.0001); for population data, the neural pitch predictions explain about 99.8% of the variation in subjective percepts with a regression line slope of 1.0 (Figure 3g, df = 1,3; F = 1614.0, p<0.0001614.0,
The CAR model predictions explain a large fraction of the variability in observed concentrations at the 13 Amsterdam sites of the large-area campaign, but a systematic overestimation of background concentrations and underestimation of local traffic contributions to concentrations is evident.
However, many human and mouse NMIs have lower CpG O/E and GC content than the CGI predictions, explaining why the CGI predictions do not accurately identify all NMIs in these species.
We focused on gastrointestinal helminth communities to determine whether these predictions could explain the ongoing invasion success of the commensal house mouse (Mus musculus domesticus) and black rat (Rattus rattus), as well as the associated decrease in native Mastomys spp., in Senegal.
These predictions may explain the rarity of cooperative behaviours in lizards and their lack of investment in long-term home construction for groups of individuals.
Thus, local interactions within a chain are mainly limited to trivial nearest neighbor contacts that are easily satisfied in predictions, which explain the higher lDDT scores.
