The worldwide marine radiocarbon reservoir effect: definitions, mechanisms, and prospects

When a carbon reservoir has a lower radiocarbon content than the atmosphere, this is referred to as a reservoir effect. This is expressed as an offset between the radiocarbon ages of samples from the two reservoirs at a single point in time. The marine reservoir effect (MRE) has been a major concern in the radiocarbon community, as it introduces an additional source of error that is often difficult to accurately quantify. For this reason, researchers are often reluctant to date marine material where they have another option. The influence of this phenomenon makes the study of the MRE important for a broad range of applications. The advent of Accelerator Mass Spectrometry (AMS) has reduced sample size requirements and increased measurement precision, in turn increasing the number of studies seeking to measure marine samples. These studies rely on overcoming the influence of the MRE on marine radiocarbon dates through the worldwide quantification of the local parameter ΔR, that is, the local variation from the global average MRE. Furthermore, the strong dependence on ocean dynamics makes the MRE a useful indicator for changes in oceanic circulation, carbon exchange between reservoirs, and the fate of atmospheric CO2, all of which impact Earth's climate. This article explores data from the Marine Reservoir Database and reviews the place of natural radiocarbon in oceanic records, focusing on key questions (e.g., changes in ocean dynamics) that have been answered by MRE studies and on their application to different subjects

Integrating Case-Based Reasoning with Adaptive Process Management

The need for more flexiblity of process-aware information systems (PAIS) has been discussed for several years and different approaches for adaptive process management have emerged. Only few of them provide support for both changes of individual process instances and the propagation of process type changes to a collection of related process instances. The knowledge about changes has not yet been exploited by any of these systems. To overcome this practical limitation, PAIS must capture the whole process life cycle and all kinds of changes in an integrated way. They must allow users to deviate from the predefined process in exceptional situations, and assist them in retrieving and reusing knowledge about previously performed changes. In this report we present a proof-of concept implementation of a learning adaptive PAIS. The prototype combines the ADEPT2 framework for dynamic process changes with concepts and methods provided by case-based reasoning(CBR) technology