title: Impacts of deforestation and burning, and the role of bracken fern, on the properties of surficial or buried soil A-horizons

authors: Lowe, David J.

subjects: bracken fern

abstract: Bracken fern (Pteridium spp.) is an aggressive plant that commonly invades disturbed sites. Its success as an invader is attributable, in part, to its ability to produce abundant growth, both below ground in the form of rhizomes and fine roots and above ground as fronds and stems. This biomass production has been shown to affect numerous soil properties. In describing soils of the „Pumice Lands‟ (Pumice Soils or Vitrands mainly) in New Zealand, Molloy and Christie (1998) attributed black A horizons „to bracken fern, which replaced much of the forest‟. Analyses of humus and phytoliths in the A horizons of soils developed especially on Kaharoa and Taupo tephras in central North Island (buried beneath 1886 Tarawera eruptives in the Rerewhakaaitu area) showed that type-A humic acids predominated and that fernland and grassland had replaced the pre-existing forests (Birrell et al., 1971; Sase et al., 1988; Hosono et al., 1991; Sase and Hosono, 1996). Pollen, phytolith and associated studies, together with tephrochronology, have shown that human-induced deforestation by burning began in New Zealand soon after Polynesian settlers arrived (e.g. McGlone, 1989; Clarkson et al., 1992; Kondo et al., 1994; McGlone et al., 1994; Newnham et al., 1998; McGlone and Wilmshurst, 1999; Watanabe and Sakagami, 1999; see also article on Polynesian settlement by Lowe, this volume). The repeated burning resulted in the formation of extensive fernlands (McGlone et al., 2005).

text: Impacts of deforestation and burning, and the role of bracken fern, on the properties of surficial or buried soil A-horizons Bracken fern (Pteridium spp.) is an aggressive plant that commonly invades disturbed sites. Its success as an invader is attributable, in part, to its ability to produce abundant growth, both below ground in the form of rhizomes and fine roots and above ground as fronds and stems. This biomass production has been shown to affect numerous soil properties. In describing soils of the „Pumice Lands‟ (Pumice Soils or Vitrands mainly) in New Zealand, Molloy and Christie (1998) attributed black A horizons „to bracken fern, which replaced much of the forest‟. Analyses of humus and phytoliths in the A horizons of soils developed especially on Kaharoa and Taupo tephras in central North Island (buried beneath 1886 Tarawera eruptives in the Rerewhakaaitu area) showed that type-A humic acids predominated and that fernland and grassland had replaced the pre-existing forests (Birrell et al., 1971; Sase et al., 1988; Hosono et al., 1991; Sase and Hosono, 1996). Pollen, phytolith and associated studies, together with tephrochronology, have shown that human-induced deforestation by burning began in New Zealand soon after Polynesian settlers arrived (e.g. McGlone, 1989; Clarkson et al., 1992; Kondo et al., 1994; McGlone et al., 1994; Newnham et al., 1998; McGlone and Wilmshurst, 1999; Watanabe and Sakagami, 1999; see also article on Polynesian settlement by Lowe, this volume). The repeated burning resulted in the formation of extensive fernlands (McGlone et al., 2005).

year: 2008

title: Book review: “England's First Family of Writers” and “Percy Bysshe Shelley: A Biography”

authors: Weston, Rowland

subjects: review

abstract: This article reviews the books: “England's First Family of Writers” and “Percy Bysshe Shelley: A Biography”

text: Book review: “England's First Family of Writers” and “Percy Bysshe Shelley: A Biography” This article reviews the books: “England's First Family of Writers” and “Percy Bysshe Shelley: A Biography”

year: 2010

title: Data and model bias in artificial intelligence for healthcare applications in New Zealand

authors: Yogarajan, Vithya

abstract: Introduction: Developments in Artificial Intelligence (AI) are adopted widely in healthcare. However, the introduction and use of AI may come with biases and disparities, resulting in concerns about healthcare access and outcomes for underrepresented indigenous populations. In New Zealand, Māori experience significant inequities in health compared to the non-Indigenous population. This research explores equity concepts and fairness measures concerning AI for healthcare in New Zealand. Methods: This research considers data and model bias in NZ-based electronic health records (EHRs). Two very distinct NZ datasets are used in this research, one obtained from one hospital and another from multiple GP practices, where clinicians obtain both datasets. To ensure research equality and fair inclusion of Māori, we combine expertise in Artificial Intelligence (AI), New Zealand clinical context, and te ao Māori. The mitigation of inequity needs to be addressed in data collection, model development, and model deployment. In this paper, we analyze data and algorithmic bias concerning data collection and model development, training and testing using health data collected by experts. We use fairness measures such as disparate impact scores, equal opportunities and equalized odds to analyze tabular data. Furthermore, token frequencies, statistical significance testing and fairness measures for word embeddings, such as WEAT and WEFE frameworks, are used to analyze bias in free-form medical text. The AI model predictions are also explained using SHAP and LIME. Results: This research analyzed fairness metrics for NZ EHRs while considering data and algorithmic bias. We show evidence of bias due to the changes made in algorithmic design. Furthermore, we observe unintentional bias due to the underlying pre-trained models used to represent text data. This research addresses some vital issues while opening up the need and opportunity for future research. Discussions: This research takes early steps toward developing a model of socially responsible and fair AI for New Zealand's population. We provided an overview of reproducible concepts that can be adopted toward any NZ population data. Furthermore, we discuss the gaps and future research avenues that will enable more focused development of fairness measures suitable for the New Zealand population's needs and social structure. One of the primary focuses of this research was ensuring fair inclusions. As such, we combine expertise in AI, clinical knowledge, and the representation of indigenous populations. This inclusion of experts will be vital moving forward, proving a stepping stone toward the integration of AI for better outcomes in healthcare.

text: Data and model bias in artificial intelligence for healthcare applications in New Zealand Introduction: Developments in Artificial Intelligence (AI) are adopted widely in healthcare. However, the introduction and use of AI may come with biases and disparities, resulting in concerns about healthcare access and outcomes for underrepresented indigenous populations. In New Zealand, Māori experience significant inequities in health compared to the non-Indigenous population. This research explores equity concepts and fairness measures concerning AI for healthcare in New Zealand. Methods: This research considers data and model bias in NZ-based electronic health records (EHRs). Two very distinct NZ datasets are used in this research, one obtained from one hospital and another from multiple GP practices, where clinicians obtain both datasets. To ensure research equality and fair inclusion of Māori, we combine expertise in Artificial Intelligence (AI), New Zealand clinical context, and te ao Māori. The mitigation of inequity needs to be addressed in data collection, model development, and model deployment. In this paper, we analyze data and algorithmic bias concerning data collection and model development, training and testing using health data collected by experts. We use fairness measures such as disparate impact scores, equal opportunities and equalized odds to analyze tabular data. Furthermore, token frequencies, statistical significance testing and fairness measures for word embeddings, such as WEAT and WEFE frameworks, are used to analyze bias in free-form medical text. The AI model predictions are also explained using SHAP and LIME. Results: This research analyzed fairness metrics for NZ EHRs while considering data and algorithmic bias. We show evidence of bias due to the changes made in algorithmic design. Furthermore, we observe unintentional bias due to the underlying pre-trained models used to represent text data. This research addresses some vital issues while opening up the need and opportunity for future research. Discussions: This research takes early steps toward developing a model of socially responsible and fair AI for New Zealand's population. We provided an overview of reproducible concepts that can be adopted toward any NZ population data. Furthermore, we discuss the gaps and future research avenues that will enable more focused development of fairness measures suitable for the New Zealand population's needs and social structure. One of the primary focuses of this research was ensuring fair inclusions. As such, we combine expertise in AI, clinical knowledge, and the representation of indigenous populations. This inclusion of experts will be vital moving forward, proving a stepping stone toward the integration of AI for better outcomes in healthcare.

year: 2022-12-22