Susan Hough, a U.S. Geological Survey seismologist and author of "Predicting the Unpredictable: The Tumultuous Science of Earthquake Prediction," explained that by the nineteen-nineties, "Earthquake 'prediction' had become the 'p' word that you didn't use in front of polite company".
The theoretical prediction explains well the observed relationship between bedrock depth and the predominant period of long-period ground motions for shallow bedrock depths (
And I believe my prediction explained wi-fi as more of "a system of tubes bearing worldwide messages from all the countries of the globe, sharing ideas and creating meaning in perfect harmony".
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.
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.
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.
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,
These predictions explained more than half of the variance in learning success among individuals, suggesting that individual differences in neuroanatomy or persistent physiology predict whether and to what extent people will benefit from training in a complex task.
Using a cross-validation approach, genomic predictions explained ~32% of the variation in yield phenotypes.
