title: Spot sampling of nutrient concentrations in the Puarenga catchment, Rotorua

authors: Abell, Jonathan Michael

subjects: New Zealand

abstract: The Centre for Biodiversity and Ecology Research was approached by Tūhourangi Tribal Authority for assistance with measuring water quality in streams in the Puarenga Stream catchment. Water sampling was subsequently undertaken on 18 July 2011 and samples were analysed to determine concentrations of total and dissolved fractions of nitrogen and phosphorus. 
Nitrogen and phosphorus are both essential plant nutrients which, when present in excess, can cause eutrophication and associated water quality decline of freshwaters. High concentrations of dissolved forms of nitrogen can also be toxic to aquatic organisms. Excessive nitrogen and phosphorus concentrations are typically the result of pollution due to human activities, although groundwater in the Central Volcanic Plateau region can have elevated concentrations of phosphorus arising from natural geological sources.
This report summarises the methods used, presents the results and places measured concentrations in context by drawing comparisons with both guideline and regional mean values.

text: Spot sampling of nutrient concentrations in the Puarenga catchment, Rotorua The Centre for Biodiversity and Ecology Research was approached by Tūhourangi Tribal Authority for assistance with measuring water quality in streams in the Puarenga Stream catchment. Water sampling was subsequently undertaken on 18 July 2011 and samples were analysed to determine concentrations of total and dissolved fractions of nitrogen and phosphorus. 
Nitrogen and phosphorus are both essential plant nutrients which, when present in excess, can cause eutrophication and associated water quality decline of freshwaters. High concentrations of dissolved forms of nitrogen can also be toxic to aquatic organisms. Excessive nitrogen and phosphorus concentrations are typically the result of pollution due to human activities, although groundwater in the Central Volcanic Plateau region can have elevated concentrations of phosphorus arising from natural geological sources.
This report summarises the methods used, presents the results and places measured concentrations in context by drawing comparisons with both guideline and regional mean values.

year: 2011-09

title: Foresight in hindsight: Visions of the future from Julius Vogel, Āpirana Ngata, Truby King, and Ettie Rout, 1889-1923

authors: Lemberg, Nicola Johanna

subjects: history

abstract: This thesis examines seven future-focused texts authored by four New Zealanders: Julius Vogel, Āpirana Ngata, Truby King, and Ettie Rout. ‘The future’ is a neglected lens for historical research and this thesis explores the value of such an approach, using New Zealand society from the 1880s and to the 1920s as a case study. A methodological combination of textual analysis, biography, and historical contexts, including consideration of print culture, allows for nuanced examination of depictions of the future, and suggests such connections are vital to fully understanding histories of the future. This research serves as a framework to inform further exploration of historical future imaginings, either in New Zealand or in other geographic or temporal contexts.

From Vogel’s novel Anno Domini 2000 (1889) to Ngata’s 1892 essay The Past and Future of the Maori to King’s and Rout’s twentieth-century guidebooks on the care of babies and the use of contraception, the four authors utilize various textual forms to communicate their imagined future, and commonly frame their ideas within concepts of utopia and dystopia. This thesis compares the futures depicted by male and female authors, and those emerging from Māori and Pākehā cultural contexts. It examines futures envisioned by politicians, community leaders, and ostracized individuals. Shaped by preoccupations with empire, gender, and identity, these future-focused texts highlight relationships between national and global spaces. This in turn allows for a fresh exploration of New Zealand’s past, and especially the country’s colonial histories.

text: Foresight in hindsight: Visions of the future from Julius Vogel, Āpirana Ngata, Truby King, and Ettie Rout, 1889-1923 This thesis examines seven future-focused texts authored by four New Zealanders: Julius Vogel, Āpirana Ngata, Truby King, and Ettie Rout. ‘The future’ is a neglected lens for historical research and this thesis explores the value of such an approach, using New Zealand society from the 1880s and to the 1920s as a case study. A methodological combination of textual analysis, biography, and historical contexts, including consideration of print culture, allows for nuanced examination of depictions of the future, and suggests such connections are vital to fully understanding histories of the future. This research serves as a framework to inform further exploration of historical future imaginings, either in New Zealand or in other geographic or temporal contexts.

From Vogel’s novel Anno Domini 2000 (1889) to Ngata’s 1892 essay The Past and Future of the Maori to King’s and Rout’s twentieth-century guidebooks on the care of babies and the use of contraception, the four authors utilize various textual forms to communicate their imagined future, and commonly frame their ideas within concepts of utopia and dystopia. This thesis compares the futures depicted by male and female authors, and those emerging from Māori and Pākehā cultural contexts. It examines futures envisioned by politicians, community leaders, and ostracized individuals. Shaped by preoccupations with empire, gender, and identity, these future-focused texts highlight relationships between national and global spaces. This in turn allows for a fresh exploration of New Zealand’s past, and especially the country’s colonial histories.

year: 2024

title: Adaptive random forests for evolving data stream classification

authors: Gomes, Heitor Murilo

subjects: Data stream mining

abstract: Random forests is currently one of the most used machine learning algorithms in the non-streaming (batch) setting. This preference is attributable to its high learning performance and low demands with respect to input preparation and hyper-parameter tuning. However, in the challenging context of evolving data streams, there is no random forests algorithm that can be considered state-of-the-art in comparison to bagging and boosting based algorithms. In this work, we present the adaptive random forest (ARF) algorithm for classification of evolving data streams. In contrast to previous attempts of replicating random forests for data stream learning, ARF includes an effective resampling method and adaptive operators that can cope with different types of concept drifts without complex optimizations for different data sets. We present experiments with a parallel implementation of ARF which has no degradation in terms of classification performance in comparison to a serial implementation, since trees and adaptive operators are independent from one another. Finally, we compare ARF with state-of-the-art algorithms in a traditional test-then-train evaluation and a novel delayed labelling evaluation, and show that ARF is accurate and uses a feasible amount of resources.

text: Adaptive random forests for evolving data stream classification Random forests is currently one of the most used machine learning algorithms in the non-streaming (batch) setting. This preference is attributable to its high learning performance and low demands with respect to input preparation and hyper-parameter tuning. However, in the challenging context of evolving data streams, there is no random forests algorithm that can be considered state-of-the-art in comparison to bagging and boosting based algorithms. In this work, we present the adaptive random forest (ARF) algorithm for classification of evolving data streams. In contrast to previous attempts of replicating random forests for data stream learning, ARF includes an effective resampling method and adaptive operators that can cope with different types of concept drifts without complex optimizations for different data sets. We present experiments with a parallel implementation of ARF which has no degradation in terms of classification performance in comparison to a serial implementation, since trees and adaptive operators are independent from one another. Finally, we compare ARF with state-of-the-art algorithms in a traditional test-then-train evaluation and a novel delayed labelling evaluation, and show that ARF is accurate and uses a feasible amount of resources.

year: 2017