title: Provenance and geochemistry of exotic clasts in conglomerates of the Oligocene Torehina Formation, Coromandel Peninsula, New Zealand

authors: Nelson, Campbell S.

subjects: Coromandel Peninsula

abstract: Non-marine pebble to cobble conglomerates of the lower Torehina Formation (Oligocene) crop out along western Coromandel Peninsula and overlie, with strong angular discordance, continental-margin metasedimentary rocks (Manaia Hill Group) of Mesozoic (Late Jurassic to ?Early Cretaceous) age. The conglomerates contain provenance information that identifies a pre-Oligocene depositional history obscured by the unconformable juxtaposition of these Tertiary and Mesozoic strata. Most clasts in the lower Torehina Formation are visually similar to local bedrock lithologies, including metamorphosed sandstones and argillites, but are kaolinitic and contain more detrital and authigenic chert, quartz, and potash feldspar. Local derivation of these clasts seems unlikely. By comparing geochemical ratios with those defined for continental margin sandstones, and well characterised New Zealand tectonic terranes, we interpret the majority of clasts in the lower Torehina Formation to have been derived from a dissected orogen, with mixtures of felsic and volcanogenic-derived sediment. The most likely sources are the Waipapa and Torlesse Terranes. The remaining 20–30% of the clasts in the lower Torehina Formation were originally friable, are coarse grained, and appear to be lithologically exotic relative to known metamorphosed sandstones in basement terrane sources on North Island. Some clasts contain coal laminae and particles, and all contain detrital kaolinite as lithic fragments and matrix. Such characteristics imply a non-marine to marginal-marine source containing sediment derived from strongly weathered granite or granodiorite. Mechanical fragility implies a likely proximal, easily erodible source. We propose that this group of clasts was derived from an Upper Cretaceous sedimentary cover, either part of a locally developed basin fill or part of a once regionally extensive cover on North Island. Either case defines a more widely distributed Cretaceous source than found today.

text: Provenance and geochemistry of exotic clasts in conglomerates of the Oligocene Torehina Formation, Coromandel Peninsula, New Zealand Non-marine pebble to cobble conglomerates of the lower Torehina Formation (Oligocene) crop out along western Coromandel Peninsula and overlie, with strong angular discordance, continental-margin metasedimentary rocks (Manaia Hill Group) of Mesozoic (Late Jurassic to ?Early Cretaceous) age. The conglomerates contain provenance information that identifies a pre-Oligocene depositional history obscured by the unconformable juxtaposition of these Tertiary and Mesozoic strata. Most clasts in the lower Torehina Formation are visually similar to local bedrock lithologies, including metamorphosed sandstones and argillites, but are kaolinitic and contain more detrital and authigenic chert, quartz, and potash feldspar. Local derivation of these clasts seems unlikely. By comparing geochemical ratios with those defined for continental margin sandstones, and well characterised New Zealand tectonic terranes, we interpret the majority of clasts in the lower Torehina Formation to have been derived from a dissected orogen, with mixtures of felsic and volcanogenic-derived sediment. The most likely sources are the Waipapa and Torlesse Terranes. The remaining 20–30% of the clasts in the lower Torehina Formation were originally friable, are coarse grained, and appear to be lithologically exotic relative to known metamorphosed sandstones in basement terrane sources on North Island. Some clasts contain coal laminae and particles, and all contain detrital kaolinite as lithic fragments and matrix. Such characteristics imply a non-marine to marginal-marine source containing sediment derived from strongly weathered granite or granodiorite. Mechanical fragility implies a likely proximal, easily erodible source. We propose that this group of clasts was derived from an Upper Cretaceous sedimentary cover, either part of a locally developed basin fill or part of a once regionally extensive cover on North Island. Either case defines a more widely distributed Cretaceous source than found today.

year: 2003

title: MOA: Massive Online Analysis

authors: Bifet, Albert

subjects: computer science

abstract: Massive Online Analysis (MOA) is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. MOA includes a collection of offline and online methods as well as tools for evaluation. In particular, it implements boosting, bagging, and Hoeffding Trees, all with and without Naïve Bayes classifiers at the leaves. MOA supports bi-directional interaction with WEKA, the Waikato Environment for Knowledge Analysis, and is released under the GNU GPL license.

text: MOA: Massive Online Analysis Massive Online Analysis (MOA) is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. MOA includes a collection of offline and online methods as well as tools for evaluation. In particular, it implements boosting, bagging, and Hoeffding Trees, all with and without Naïve Bayes classifiers at the leaves. MOA supports bi-directional interaction with WEKA, the Waikato Environment for Knowledge Analysis, and is released under the GNU GPL license.

year: 2010

title: Does artificial intelligence modelling have anything to offer traditional management of freshwater food resources?

authors: Death, Russell G.

abstract: Management of freshwater systems and the ecosystem services they provide has become a multi-stakeholder activity. This requires information on resources and how to manage them to be disseminated to a wide range of users. While artificial intelligence modelling can provide a powerful tool in managing and understanding resources and their drivers, they can be confusing to many users. In this study, we explored the potential use of two alternative modelling approaches (Boosted Regression Trees (BRT) and Bayesian Belief Networks (BBN)) for managing three species of freshwater mahinga kai species-kākahi or kaeo (freshwater mussel), koura (freshwater crayfish) and tuna (freshwater eel). While the BBN model I sbetter for stakeholder communication, the BRT produced more accurate models for all species. However, variables identified as being important for predicting abundance and biomass of these species were often environmental parameters that cannot be managed to improve yield. The artificial intelligence modelling does provide some accurate linkages between the target species and their environmental drivers. Nevertheless, translating these relationships into management plans remains challenging. The models are clearly not a panacea for better resource management, but provide one more tool that might assist multi-stakeholder understanding of how best to manage freshwater resources.

text: Does artificial intelligence modelling have anything to offer traditional management of freshwater food resources? Management of freshwater systems and the ecosystem services they provide has become a multi-stakeholder activity. This requires information on resources and how to manage them to be disseminated to a wide range of users. While artificial intelligence modelling can provide a powerful tool in managing and understanding resources and their drivers, they can be confusing to many users. In this study, we explored the potential use of two alternative modelling approaches (Boosted Regression Trees (BRT) and Bayesian Belief Networks (BBN)) for managing three species of freshwater mahinga kai species-kākahi or kaeo (freshwater mussel), koura (freshwater crayfish) and tuna (freshwater eel). While the BBN model I sbetter for stakeholder communication, the BRT produced more accurate models for all species. However, variables identified as being important for predicting abundance and biomass of these species were often environmental parameters that cannot be managed to improve yield. The artificial intelligence modelling does provide some accurate linkages between the target species and their environmental drivers. Nevertheless, translating these relationships into management plans remains challenging. The models are clearly not a panacea for better resource management, but provide one more tool that might assist multi-stakeholder understanding of how best to manage freshwater resources.

year: 2017