The same can be said of the relationships between different degrees of measurement invariance, different measurement models, and other (more general) prediction models.
A more general prediction could be that splicing of exons with higher densities of total number of ESRs (and thus more trans-factor binding sites) would be more sensitive to changes in trans-factors; however, we found no correlation between exonic ESR density and change in AS levels.
In Text S12 we present some support for the more general prediction that the most frequent typologies (e.g., case suffixes are much more widespread/frequent than case prefixes) correspond to those known to be more easily learned by children whereas typologies common to high-population (i.e., exoteric) languages are those that are best learned by adults (see also Figure S2; Table S2).
The more general prediction of an association between ADHD and choice impulsivity is not specific to delay aversion theory but is shared for example with the Dynamic Developmental Theory of Sagvolden and colleagues (see below).
By using a reciprocal transplant design, where epiphyte mats were transplanted across an altitudinal gradient of increasing cloud immersion, we differentiated between the effects of warmer and drier conditions from the more general prediction of niche theory that transplanting epiphytes in any direction away from their home elevation should result in reduced performance.
This last result is at odds with the more general prediction that forest ecosystems should have greater fungal dominance of soil energy channels than grasslands (Imberger and Chiu 2001; Harris 2009) and observations of higher fungal dominance in young planted Pinus radiata compared with pasture (Macdonald et al. 2009).
More frequently, general predictions can be found, e.g. regarding future water consumption, food production and consumption behaviour.
In order to further identify distantly homologous miRNAs, a probabilistic co-learning method using a paired hidden Markov model (HMM) was recently developed as a more general miRNA prediction method (Nam et al., 2005).
Focusing on functions, rather than on technology and performance, it complements standard S-curve based analyses and helps to make more abstract and general predictions on the evolution of technology.
Although predicting survival might be important in a more general sense, the prediction of response to specific therapies is of far greater clinical value.
