Unification of New Zealand's local vertical datums: iterative gravimetric quasigeoid computations

New Zealand uses 13 separate local vertical datums (LVDs) based on normal-orthometric-corrected precise geodetic levelling from 12 different tide-gauges. We describe their unification using a regional gravimetric quasigeoid model and GPS-levelling data on each LVD. A novel application of iterative quasigeoid computation is used, where the LVD offsets computed from earlier models are used to apply additional gravity reductions from each LVD to that model. The solution converges after only three iterations yielding LVD offsets ranging from 0.24 m to 0.58 m with an average standard deviation of 0.08 m. The so-computed LVD offsets agree, within expected data errors, with geodetically levelled height differences at common benchmarks between adjacent LVDs. This shows that iterated quasigeoid models do have a role in vertical datum unification

Habitat partitioning and vulnerability of sharks in the Great Barrier Reef Marine Park

Sharks present a critical conservation challenge, but little is known about their spatial distribution and vulnerability, particularly in complex seascapes such as Australia's Great Barrier Reef Marine Park (GBRMP). We review (1) the distribution of shark species among the primary habitats of the GBRMP (coral reefs, inshore/shelf, pelagic and deep-water habitats) (2) the relative exploitation of each species by fisheries, and (3) how current catch rates interact with their vulnerability and trophic index. Excluding rays and chimaeras, we identify a total of 82 shark species in the GBRMP. We find that shark research in the GBRMP has yielded little quantitative information on most species. Reef sharks are largely site-fidelic, but can move large distances and some regularly use non-reef habitats. Inshore and shelf sharks use coastal habitats either exclusively or during specific times in their life cycle (e.g. as nurseries). Virtually nothing is known about the distribution and habitat use of the GBRMP's pelagic and deep-water sharks. At least 46 species (53.5 %) are caught in one or more fisheries, but stock assessments are lacking for most. At least 17 of the sharks caught are considered highly vulnerable to exploitation. We argue that users of shark resources should be responsible for demonstrating that a fishery is sustainable before exploitation is allowed to commence or continue. This fundamental change in management principle will safeguard against stock collapses that have characterised many shark fisheries

Artificial intelligence assistants and risk: framing a connectivity risk narrative

Our social relations are changing, we are now not just talking to each other, but we are now also talking to artificial intelligence (AI) assistants. We claim AI assistants present a new form of digital connectivity risk and a key aspect of this risk phenomenon relates to user risk awareness (or lack of) regarding AI assistant functionality. AI assistants present a significant societal risk phenomenon amplified by the global scale of the products and the increasing use in healthcare, education, business, and service industry. However, there appears to be little research concerning the need to not only understand the risks of AI assistant technologies but also how to frame and communicate the risks to users. How can users assess the risks without fully understanding the complexity of the technology? This is a challenging and unwelcome scenario. AI assistant technologies consists of a complex eco-system and demands explicit and precise communication in terms of communicating and contextualising the new digital risk phenomenon. The paper then agues for the need to examine how to best to explain and support both domestic and commercial user risk awareness regarding AI assistants. To this end, we propose the method of creating a risk narrative which is focused on temporal points of changing societal connectivity and contextualised in terms of risk. We claim the connectivity risk narrative provides an effective medium in capturing, communicating, and contextualising the risks of AI assistants in a medium that can support explainability as a risk mitigation mechanism

Risk communication and the social amplification of risk

Risk communication is a novel concept in the scientific pursuit to understand and analyze risk related decisions and behavior in modem society. But the new term has only changed the focus of attention from a static description of what risk means for different communities to a dynamic analysis on how these communities exchange information about risk and adjust their behavior.The concept of social amplification of risk provides a framework for the analysis of communication as well as other social activities and constitutes a dynamic model which facilitates the systematic interpretation of empirical data and attempts to integrate the existing perspectives into a higher-order terminological model. The concept will certainly not encompass all perspectives,and it will not be capable of unifying different scientific camps