Why do we need 14 C inter-comparisons?: The Glasgow - 14 C inter-comparison series, a reflection over 30 years

Abstract

Radiocarbon measurement is a well-established, routinely used, yet complex series of interlinked procedures. The degree of sample pre-treatment varies considerably depending on the material, the methods of processing pre-treated material vary across laboratories and the detection of 14 C at low levels remains challenging. As in any complex measurement process, the questions of quality assurance and quality control become paramount, both internally, i.e. within a laboratory and externally, across laboratories. The issue of comparability of measurements (and thus bias, accuracy and precision of measurement) from the diverse laboratories is one that has been the focus of considerable attention for some time, both within the 14 C community and the wider user communities. In the early years of the technique when there was only a small number of laboratories in existence, inter-comparisons would function on an ad hoc basis, usually involving small numbers of laboratories (e.g.Otlet et al, 1980). However, as more laboratories were setup and the detection methods were further developed (e.g. new AMS facilities), the need for more systematic work was recognised. The international efforts to create a global calibration curve also requires the use of data generated by different laboratories at different times, so that evidence of laboratory offsets is needed to inform curve formation. As a result of these factors, but also as part of general good laboratory practice, including laboratory benchmarking and quality assurance, the 14 C community has undertaken a wide-scale, farreaching and evolving programme of global inter-comparisons, to the benefit of laboratories and users alike. This paper looks at some of that history and considers what has been achieved in the past 30 years.

Life After SIRI—Where Next?

“…When results are considered as a whole, then laboratories can identify areas of strength or weakness, and can demonstrate the validity of the results. https://www.nist.gov/blogs/blogrige/how-do-you-harness-power-benchmarking Our more recent work (Scott et al, 2013(Scott et al, , 2018 has provided z-scores as a means of benchmarking. The z-score is calculated relative to the sample consensus value and incorporates random and systematic uncertainties where z-score= (xm-xA)/ p…”

“…Our consensus values provide both a specified activity/age but also the associated uncertainty. The procedures now used to calculate the consensus values have changed from the early studies since, with the predominance of AMS laboratories, we have introduced a linear mixed model approach (Scott et al, 2017(Scott et al, , 2018 which appropriately accommodates shared sources of uncertainty (such as more than one result being reported, or a single AMS facility being used by several laboratories, etc). In SIRI, unlike earlier studies, we have used a random effects model, this model allows us to include key information on the multiple measurements reported on the same material by each laboratory, which is increasingly relevant since in SIRI, the vast majority of laboratories were AMS.…”

Comparative analysis of Middle Stone Age artifacts in Africa (CoMSAfrica)

“…Otherwise, we might be comparing differences in the behavior of contemporary archeologists rather than that of ancient hominins. We have initiated a round‐robin replication study in which all workshop participants analyze an experimentally generated flake assemblage, using our 40‐attribute list to evaluate and quantify interobserver error on each measure, similar to interlaboratory studies done for radiocarbon dating and tephra correlation …”

IntCal20 Tree Rings: An Archaeological Swot Analysis

“…The accuracy of the measurements that are calibrated or modeled against IntCal20 is, however, a material factor in whether the chronologies produced are correct. Given that the overdispersion of the IntCal20 tree-ring data, even including the factors additional to inter-laboratory variation included in this estimate described above, is one fifth of that for tree-ring measurements reported in the international inter-laboratory comparison exercises (Scott et al 2018;Heaton et al 2020 in this issue: figure 5), most laboratories are clearly not producing measurements of equivalent accuracy to those included in the calibration datasets.…”