The direct carbonate procedure for accelerator mass spectrometry radiocarbon (AMS 14C) dating of submilligram samples of biogenic carbonate without graphitization is becoming widely used in a variety of studies. We compare the results of 153 paired direct carbonate and standard graphite 14C determinations on single specimens of an assortment of biogenic carbonates. A reduced major axis regression shows a strong relationship between direct carbonate and graphite percent Modern Carbon (pMC) values (m = 0.996; 95% CI [0.991–1.001]). An analysis of differences and a 95% confidence interval on pMC values reveals that there is no significant difference between direct carbonate and graphite pMC values for 76% of analyzed specimens, although variation in direct carbonate pMC is underestimated. The difference between the two methods is typically within 2 pMC, with 61% of direct carbonate pMC measurements being higher than their paired graphite counterpart. Of the 36 specimens that did yield significant differences, all but three missed the 95% significance threshold by 1.2 pMC or less. These results show that direct carbonate 14C dating of biogenic carbonates is a cost-effective and efficient complement to standard graphite 14C dating.

We propose to derive deviation measures through the Minkowski gauge of a given set of acceptable positions. We show that given a suitable acceptance set, any positive homogeneous deviation measure can be accommodated in our framework. In doing so, we provide a new interpretation for such measures, namely, that they quantify how much one must shrink a position for it to become acceptable. In particular, the Minkowski gauge of a set which is convex, stable under scalar addition, and radially bounded at non-constants, is a generalized deviation measure. Furthermore, we explore the relations existing between mathematical and financial properties attributable to an acceptance set on the one hand, and the corresponding properties of the induced measure on the other. Dual characterizations in terms of polar sets and support functionals are provided.

No quadro da chegada da Covid-19 no Rio Grande do Sul (RS), em março de 2020, iniciamos o monitoramento da pandemia utilizando um Sistema de Informação Geográfica (SIG) online. Ele foi desenvolvido dentro do Projeto SIG Litoral, da Universidade Federal do Rio Grande do Sul, com a estratégia de disponibilizar à sociedade análises espaciais e dados organizados sobre a doença, reunindo pesquisadoras/es, bolsistas e voluntárias/os. O portal, voltado à comunicação em saúde, possibilita a visualização de dados nas escalas global, nacional e regional, além do monitoramento das populações indígenas da Amazônia e o mapeamento das redes de solidariedade no Litoral Norte do RS. Nesse artigo, enfocamos principalmente o monitoramento da Covid-19 no RS, abordando as ferramentas utilizadas, os desafios para obtenção de dados e algumas análises espaciais realizadas. Mostramos divergências entre as fontes de dados, indicando o problema da subnotificação e as diferentes metodologias empregadas pelos sistemas de vigilância sanitária. Por fim, mostramos alguns indicadores sobre números de acessos ao SIG e alcance geográfico do monitoramento da Covid-19 pela UFRGS, repercutindo positivamente na sociedade ao dar transparência aos dados oficiais, contribuindo para a pluralidade do debate sobre a pandemia, a interação de equipes multidisciplinares e a formação acadêmica de estudantes.