2 resultados para Automorphism Groups
em DigitalCommons - The University of Maine Research
Resumo:
Minerals isostructural with sapphirine-1A, sapphirine-2M, and surinamite are closely related chain silicates that pose nomenclature problems because of the large number of sites and potential constituents, including several (Be, B, As, Sb) that are rare or absent in other chain silicates. Our recommended nomenclature for the sapphirine group (formerly-aenigmatite group) makes extensive use of precedent, but applies the rules to all known natural compositions, with flexibility to allow for yet undiscovered compositions such as those reported in synthetic materials. These minerals are part of a polysomatic series composed of pyroxene or pyroxene-like and spinel modules, and thus we recommend that the sapphirine supergroup should encompass the polysomatic series. The first level in the classification is based on polysome, i.e. each group within the supergroup Corresponds to a single polysome. At the second level, the sapphirine group is divided into subgroups according to the occupancy of the two largest M sites, namely, sapphirine (Mg), aenigmatite (Na), and rhonite (Ca). Classification at the third level is based on the occupancy of the smallest M site with most shared edges, M7, at which the dominant cation is most often Ti (aenigmatite, rhonite, makarochkinite), Fe(3+) (wilkinsonite, dorrite, hogtuvaite) or Al (sapphirine, khmaralite); much less common is Cr (krinovite) and Sb (welshite). At the fourth level, the two most polymerized T sites are considered together, e.g. ordering of Be at these sites distinguishes hogtuvaite, makarochkinite and khmaralite. Classification at the fifth level is based on X(Mg) = Mg/(Mg + Fe(2+)) at the M sites (excluding the two largest and M7). In principle, this criterion could be expanded to include other divalent cations at these sites, e.g. Mn. To date, most minerals have been found to be either Mg-dominant (X(mg) > 0.5), or Fe(2+)-dominant (X(Mg) < 0.5), at these M sites. However, X(mg) ranges from 1.00 to 0.03 in material described as rhonite, i.e. there are two species present, one Mg-dominant, the other Fe(2+)-dominant. Three other potentially new species are a Mg-dominant analogue of wilkinsonite, rhonite in the Allende meteorite, which is distinguished front rhonite and dorrite in that Mg rather than Ti or FC(3+) is dominant at M7, and an Al-dominant analogue of sapphirine, in which Al > Si at the two most polymerized T sites vs. Al < Si in sapphirine. Further splitting of the supergroup based on occupancies other than those specified above is not recommended.
Resumo:
Application of biogeochemical models to the study of marine ecosystems is pervasive, yet objective quantification of these models' performance is rare. Here, 12 lower trophic level models of varying complexity are objectively assessed in two distinct regions (equatorial Pacific and Arabian Sea). Each model was run within an identical one-dimensional physical framework. A consistent variational adjoint implementation assimilating chlorophyll-a, nitrate, export, and primary productivity was applied and the same metrics were used to assess model skill. Experiments were performed in which data were assimilated from each site individually and from both sites simultaneously. A cross-validation experiment was also conducted whereby data were assimilated from one site and the resulting optimal parameters were used to generate a simulation for the second site. When a single pelagic regime is considered, the simplest models fit the data as well as those with multiple phytoplankton functional groups. However, those with multiple phytoplankton functional groups produced lower misfits when the models are required to simulate both regimes using identical parameter values. The cross-validation experiments revealed that as long as only a few key biogeochemical parameters were optimized, the models with greater phytoplankton complexity were generally more portable. Furthermore, models with multiple zooplankton compartments did not necessarily outperform models with single zooplankton compartments, even when zooplankton biomass data are assimilated. Finally, even when different models produced similar least squares model-data misfits, they often did so via very different element flow pathways, highlighting the need for more comprehensive data sets that uniquely constrain these pathways.