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    The problem of a nonuniform distribution of muscovite-poor rocks (e.g., pure marble) would only exacerbate the mismatch between t * and z *. This may not be a problem for the samples from small basins, but the samples discussed here with large basins that include substantial proportions of the LHS may be not well sampled by the coarse muscovites used in most detrital studies. As discussed here, the technical necessity of restricting single-crystal analysis to coarse-grained muscovites (which are less common in the LHS in our observation) will bias the population toward the GHS and away from the LHS.

    Although the statistical analysis of Table 3 suggests that only a few of the basins discussed here come close to a rigorous definition of a good fit between the distribution of elevation and muscovite ages, an inspection of Figure 6 shows that some fits are clearly better than others. Figure 6 offers a bootstrap confidence assessment by qualitatively checking how well the t * overlaps with the cloud produced by the 300 random samplings of the topography. The samples with the best statistics in Table 3 have a relatively good fit using this measure (samples Nar, Nyadi, S5, S12, and S37). The basins corresponding to these samples have areas that range from 180 to 1627 km 2 . One might expect this relationship to get worse with increasing basin area, but this is not the case, particularly for the six new samples reported here. The two samples from very small catchments (MO-50 and MO-81) fail the bootstrap measure of overlap over most of the range of t * and z *, but the two largest basins (corresponding to NAG-12 and MO-217) have t * that overlaps with the z * cloud about ¾ of the distribution for the CDF but do not overlap nearly as well for the PDP ( Figs. 6A, 6B ). The two medium-sized basins (Guy-2 and MO-139) have the worst correspondence ( Figs. 6C, 6D ). Rather than suggesting that the assumptions of Ruhl and Hodges (2005) are met better for big basins than for small, the better fit of z * and t * for the largest basins suggests an averaging over space (in particular over a range of elevations) and time not possible in small basins (see Gabet et al., 2008 ).

    The overall poor correspondence between t * based on muscovite ages and z * in central Nepal suggests that the cooling from ∼400 °C to surface temperatures requires so much time (even at Himalayan erosion rates) that post–muscovite closure deformation of the region (even relatively small regions) will be a much stronger factor in the geographic distribution of muscovite ages than a simple age-elevation relationship. However, t * distributions based on other thermochronometers with lower closure temperatures such as zircon or apatite dated by either the fission track (FT) or (U-Th)/He method may give better and more-common correspondence to z *. In such cases, valuable and nuanced tectonic interpretations may be forthcoming. We do not mean to suggest that dating of modern detrital muscovites is without utility; far from it (see following). However, due to the high T c of Ar in muscovite, the instances in which one can do things such as calculate basin-wide erosion rates from the range of ages and the range of elevations seem to be the exception rather than the rule (see Braun, 2005 ).

    In Studies 3 and 4, the original countermeasures reduced performance impairment more than did the optimal strategies near the middle of the TSD challenge (i.e. the predicted mean RT for the original studies was below the mean RT of the optimal strategies; Figure 5 ). However, performance impairment was substantially greater for the original studies than for the optimal strategies during the last 6hr of the TSD challenges. Overall, the optimal strategies improved performance by 16% and 41% compared with the original countermeasures for Studies 3 and 4, respectively (Table 2 ).

    Figure Leather Statement Clutch Rose Petals by VIDA VIDA zaNYMvS1Nd
    shows the predicted mean RT profiles for the original studies (dashed orange line) and the optimal strategies (blue lines) that attempted to reduce caffeine consumption, while achieving at least the same benefit as the original studies (Figure Handkerchief from Tieroom Notch PONTUS white stripes red base Notch Handkerchief from Tieroom pYYSx
    , green arrows). Table 2 (columns 6 and 7) shows the changes in caffeine consumption for the optimal dosing strategies. In general, the optimal strategies required less caffeine consumption than the original studies.

    Figure 6
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    Optimal strategies to reduce caffeine consumption dosing while maintaining at least the same performance as in the original studies. The dashed orange lines and the continuous blue lines represent the unified model of performance predictions of the psychomotor vigilance task (PVT) mean response time (RT) for the original study and optimal strategies, respectively. The orange dots and bars represent the experimental PVT mean RT data and one standard error, respectively. The orange and blue arrows at the top of each plot indicate the time and amount of the caffeine doses for the original study and optimal strategies, respectively. The horizontal dashed lines indicate the baseline and the grey vertical bars represent time in bed. CSR, chronic sleep restriction
    Optimal strategies to reduce caffeine consumption dosing while maintaining at least the same performance as in the original studies. The dashed orange lines and the continuous blue lines represent the unified model of performance predictions of the psychomotor vigilance task (PVT) mean response time (RT) for the original study and optimal strategies, respectively. The orange dots and bars represent the experimental PVT mean RT data and one standard error, respectively. The orange and blue arrows at the top of each plot indicate the time and amount of the caffeine doses for the original study and optimal strategies, respectively. The horizontal dashed lines indicate the baseline and the grey vertical bars represent time in bed. CSR, chronic sleep restriction

    For Study 1, the total amount of caffeine used in the optimal strategy was only 700mg (Figure 6 , blue arrows), which was 65% less than that used in the original study (Table 2 , column 7), but still yielded a slightly better performance ( Z : 61 versus 64) than the original. In Study 2, the optimal strategy used 500mg (21%) less caffeine than did the original study (Table 2 , column 7), with the major caffeine savings occurring in the third wake period because it was the shortest period (Figure 6 ).

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