On the bias produced by quality scores in meta-analysis,and a hierarchical view of proposed solutions

Results from better quality studies should in some sense be more valid or more accurate than results from other studies, and as a consequence should tend to be distributed differently from results of other studies. To date, however, quality scores have been poor predictors of study results. We discuss possible reasons and remedies for this problem. It appears that 'quality' (whatever leads to more valid results) is of fairly high dimension and possibly non-additive and nonlinear, and that quality dimensions are highly application-specific and hard to measure from published information. Unfortunately, quality scores are often used to contrast, model, or modify meta-analysis results without regard to the aforementioned problems, as when used to directly modify weights or contributions of individual studies in an ad hoc manner. Even if quality would be captured in one dimension, use of quality scores in summarization weights would produce biased estimates of effect. Only if this bias were more than offset by variance reduction would such use be justified. From this perspective, quality weighting should be evaluated against formal bias-variance trade-off methods such as hierarchical (random-coefficient) meta-regression. Because it is unlikely that a low-dimensional appraisal will ever be adequate (especially over different applications), we argue that response-surface estimation based on quality items is preferable to quality weighting. Quality scores may be useful in the second stage of a hierarchical response-surface model, but only if the scores are reconstructed to maximize their correlation with bias.     

A new technique for estimating rates of carboxylation and electron transport in leaves of C3 plants for use in dynamic global vegetation models

The possible responses of the terrestrial biosphere to future CO₂ increases and associated climatic change are being investigated using dynamic global vegetation models (DG VMs) which include the Farquhar et al. (1980) biochemical model of leaf assimilation as the primary means of carbon capture. This model requires representative values of the maximum rates of Rubisco activity, V_max, and electron transport, J_max, for different vegetation types when applied at the global scale. Here, we describe an approach for calculating these values based on measurements of the maximum rate of leaf photosynthesis (A_max) ¹³C discrimination. The approach is tested and validated by comparison with measurements of Rubisco activity assayed directly on wild-type and transgenic Nicotiana tabacum (tobacco) plants with altered Rubisco activity grown under ambient and elevated CO₂ mole fractions with high and low N-supply. V_max and J_max values are reported for 18 different vegetation types with global coverage. Both variables were linearly related reinforcing the idea of optimal allocation of resources to photosynthesis (light harvesting vs. Rubisco) at the global scale. The reported figures should be of value to the further development of vegetation and ecosystem models employing mechanistic DGVMs.     

Application of equilibrium and mass transfer models to dynamic removal of Cr(VI) ions by Chitin in packed column reactor

The dynamic removal of hexavalent chromium by chitin flakes was studied in a packed column reactor. The values of column parameters were predicted as a function of flow rate, bed depth, particle size and inlet metal ion concentration. On evaluating the breakthrough curves, sorption isotherms were obtained and modelled according to the Langmuir, the Redlich–Peterson and the Freundlich models. Kinetic and mass transfer aspects of the dynamic removal of Cr(VI) ions by chitin were investigated using several mathematical models. Column studies showed a good correlation between the experimental data and the calculated breakthrough curves obtained by the Adams–Bohart or the Wolborska models and the Clark model. The simulation of the whole breakthrough curve was effective with the Clark model, but the breakthrough was best predicted by Adams–Bohart, or related derived models.