Towards an improvement of optically stimulated luminescence (OSL) age uncertainties: modelling OSL ages with systematic errors, stratigraphic constraints and radiocarbon ages using the R package BayLum

Statistical analysis has become increasingly important in optically stimulated luminescence (OSL) dating since it has become possible to measure signals at the single-grain scale. The accuracy of large chronological datasets can benefit from the inclusion, in chronological modelling, of stratigraphic constraints and shared systematic errors. Recently, a number of Bayesian models have been developed for OSL age calculation; the R package “BayLum” presented herein allows different models of this type to be implemented, particularly for samples in stratigraphic order which share systematic errors. We first show how to introduce stratigraphic constraints in BayLum; then, we focus on the construction, based on measurement uncertainties, of dose covariance matrices to account for systematic errors specific to OSL dating. The nature (systematic versus random) of errors affecting OSL ages is discussed, based – as an example – on the dose rate determination procedure at the IRAMAT-CRP2A laboratory (Bordeaux). The effects of the stratigraphic constraints and dose covariance matrices are illustrated on example datasets. In particular, the benefit of combining the modelling of systematic errors with independent ages, unaffected by these errors, is demonstrated. Finally, we discuss other common ways of estimating dose rates and how they may be taken into account in the covariance matrix by other potential users and laboratories. Test datasets are provided as a Supplement to the reader, together with an R markdown tutorial allowing the reproduction of all calculations and figures presented in this study.

The Broadening Protection Gap for Stateless Palestinian Refugees in Belgium

This paper reflects upon the issue of statelessness, Palestinians and a recent evolution of Belgian caselaw. When seeking to apply the definition of a ‘stateless person’, as found in art 1 of the 1954 Convention Relating to the Status of Stateless Persons to Palestinians, judges are confronted with specific challenges. Since 2016, divergent standards are developing as to the question of whether, and in which circumstances, Palestinians may be stateless for the purposes of international law. This evolution takes place in a national landscape characterised by a statelessness determination procedure that falls short of standards set out in the United Nations High Commissioner for Refugees Handbook on Protection of Stateless Persons in a number of areas, while a growing number of asylum seekers originating from Palestine are registered over the period 2016–19. This paper exposes, anno 2020, the protection gaps left open by the remarkably divergent approaches to this question taken by the different national actors involved.

Towards an improvement of OSL age uncertainties: modelling OSL ages with systematic errors, stratigraphic constraints and radiocarbon ages using the R package BayLum

Statistical analysis has become increasingly important in the field of OSL dating since it has become possible to measure signals at the single grain scale. The accuracy of large chronological datasets can benefit from the inclusion, in chronological modelling, of stratigraphic constraints and shared systematic errors. Recently, a number of Bayesian models have been developed for OSL age calculation; the R package BayLum allows implementing different such models, in particular for samples in stratigraphic order which share systematic errors. We first show how to introduce stratigraphic constraints in BayLum; then, we focus on the construction, based on measurement uncertainties, of dose covariance matrices to account for systematic errors specific to OSL dating. The nature (systematic versus random) of errors affecting OSL ages is discussed, based – as an example – on the dose rate determination procedure at the IRAMAT-CRP2A laboratory (Bordeaux). The effects of the stratigraphic constraints and dose covariance matrices are illustrated on example datasets. In particular, the interest of combining the modelling of systematic errors with independent ages, unaffected by these errors, is demonstrated. Finally, we discuss other common ways of estimating dose rates and how they may be taken into account in the covariance matrix by other potential users and laboratories. Test datasets are provided as supplementary material to the reader, together with an R Markdown tutorial allowing to reproduce all calculations and figures presented in this study.