Organochlorine and heavy metal concentrations in blubber and liver tissue collected from Queensland (Australia) dugong (Dugong dugon)

Tissue samples of liver and blubber were salvaged from fifty-three dugong (Dugong dugon) carcasses stranded along the Queensland coast between 1996 and 2000. Liver tissue was analysed for a range of heavy metals and blubber samples were analysed for organochlorine compounds. Metal concentrations were similar in male and female animals and were generally highest in mature animals. Liver concentrations of arsenic, chromium, iron, lead, manganese, mercury and nickel in a number of individual animals were elevated in comparison to concentrations previously reported in Australian dugong. Dieldrin, DDT (and its breakdown products) and/or heptachlor epoxide were detected in 59% of dugong blubber samples. In general, concentrations of organochlorines were similar to those reported in dugong 20 years earlier, and were low in comparison to concentrations recorded from marine mammal tissue collected elsewhere in the world. With the exception of lead, the extent of carcass decomposition, the presence of disease or evidence of animal starvation prior to death did not significantly affect dugong tissue concentrations of metals or organochlorines. The results of the study suggest that bioaccumulation of metals and organochlorine compounds (other than dioxins) does not represent a significant risk to Great Barrier Reef dugong populations, particularly in the context of other pressures associated with coastal development and other anthropogenic activities. (c) 2004 Elsevier Ltd. All rights reserved.

How does your model grow? An empirical investigation of the effects of ontological clarity and application domain size on query performance

Semantic data models provide a map of the components of an information system. The characteristics of these models affect their usefulness for various tasks (e.g., information retrieval). The quality of information retrieval has obvious important consequences, both economic and otherwise. Traditionally, data base designers have produced parsimonious logical data models. In spite of their increased size, ontologically clearer conceptual models have been shown to facilitate better performance for both problem solving and information retrieval tasks in experimental settings. The experiments producing evidence of enhanced performance for ontologically clearer models have, however, used application domains of modest size. Data models in organizational settings are likely to be substantially larger than those used in these experiments. This research used an experiment to investigate whether the benefits of improved information retrieval performance associated with ontologically clearer models are robust as the size of the application domains increase. The experiment used an application domain of approximately twice the size as tested in prior experiments. The results indicate that, relative to the users of the parsimonious implementation, end users of the ontologically clearer implementation made significantly more semantic errors, took significantly more time to compose their queries, and were significantly less confident in the accuracy of their queries.