title: Editorial: What lies beneath… confessional narratives; lessons from research.

authors: Fraser, Deborah

subjects: education

abstract: Presents an introduction to the January 2005 issue of the "Waikato Journal of Education."

text: Editorial: What lies beneath… confessional narratives; lessons from research. Presents an introduction to the January 2005 issue of the "Waikato Journal of Education."

year: 2005

title: Suspended solid levels in two chemically dosed sediment retention ponds during earthworks at SH20, Auckland

authors: Jackson, Kate Maree

subjects: Polyaluminium chloride

abstract: Earthworking activities have the potential to accelerate soil erosion through vegetation clearance and soil compaction processes. The eroded sediment can have many detrimental effects on receiving aquatic environments, and thus its discharge is controlled under the Resource Management Act, 1991. Two chemically dosed sediment retentions ponds at the SH20 extension project in Mount Roskill, Auckland were investigated, and the impact of the discharge of one of these ponds on a receiving waterbody was assessed using the Stream Ecological Valuation (SEV) method.

Rainfall and suspended solid data was collected for a nine month period between November 2006 and August 2007, although sampling did not commence at one of the ponds until March 2007. Two SEV samples were undertaken within the receiving waterbody; one in November 2006 and the other in November 2007 to assess environmental changes resulting from the sediment retention pond discharge.

The suspended solids results measured within the sediment retention ponds during this study were much lower than those reported by other studies on earthwork sites. This is believed to be due to the effective implementation of sediment and erosion control measures onsite.

The Somerset Road pond was very effective at removing suspended solids throughout the sampling period, with the majority of suspended solid removal occurring in the forebay as it typically did not become full enough to overflow into the main pond. When the forebay was full of water, the PAC dosing system resulted in large reductions in suspended solid levels over a short horizontal distance within the forebay. A smaller amount of suspended solid reduction was achieved in the main pond, predominately through dilution, with the major function of the main pond being additional storage capacity for runoff. Discharge from the Somerset Road Pond was not continuous due to low water levels in the main pond. However, when discharge did occur, the suspended solids levels were very low compared with other studies investigating sediment retention pond discharge.

The Richardson Road pond was less effective at removing suspended solids due to the flow regime within the forebay. There were two runoff channels entering the forebay, as well as a continual flow of groundwater. Only one of the runoff channels was directly dosed with PAC, and as the water level in the forebay was typically at, or just below, the level spreader at all times, there was a decreased potential for the PAC to become evenly distributed through the forebay and achieve dosing of all runoff. Furthermore, the main pond discharged continuously during the study period, resulting in reduced residence times of runoff within the pond system. Nonetheless, the discharge from the main pond was much lower than other studies, implying suspended solid reduction was being achieved.

The SEV method indicated that the receiving environment was already degraded due to modifications to the riparian vegetation, increased dissolved oxygen demand, and moderate bank erosion. This was reflected in the macroinvertebrate population, with only pollution tolerant taxa being collected, thus limiting the use of macroinvertebrates as an assessment tool in this study. However, the SEV method, which assesses a wide range of ecological functions, implied that very little environmental change occurred as a result of the sediment retention pond discharge. A small increase in deposited sediment was observed on the stream bed, however indications are that deposited sediment is rapidly washed away once earthworks are completed. Thus this deposited sediment may not have a permanent impact within the receiving environment.

text: Suspended solid levels in two chemically dosed sediment retention ponds during earthworks at SH20, Auckland Earthworking activities have the potential to accelerate soil erosion through vegetation clearance and soil compaction processes. The eroded sediment can have many detrimental effects on receiving aquatic environments, and thus its discharge is controlled under the Resource Management Act, 1991. Two chemically dosed sediment retentions ponds at the SH20 extension project in Mount Roskill, Auckland were investigated, and the impact of the discharge of one of these ponds on a receiving waterbody was assessed using the Stream Ecological Valuation (SEV) method.

Rainfall and suspended solid data was collected for a nine month period between November 2006 and August 2007, although sampling did not commence at one of the ponds until March 2007. Two SEV samples were undertaken within the receiving waterbody; one in November 2006 and the other in November 2007 to assess environmental changes resulting from the sediment retention pond discharge.

The suspended solids results measured within the sediment retention ponds during this study were much lower than those reported by other studies on earthwork sites. This is believed to be due to the effective implementation of sediment and erosion control measures onsite.

The Somerset Road pond was very effective at removing suspended solids throughout the sampling period, with the majority of suspended solid removal occurring in the forebay as it typically did not become full enough to overflow into the main pond. When the forebay was full of water, the PAC dosing system resulted in large reductions in suspended solid levels over a short horizontal distance within the forebay. A smaller amount of suspended solid reduction was achieved in the main pond, predominately through dilution, with the major function of the main pond being additional storage capacity for runoff. Discharge from the Somerset Road Pond was not continuous due to low water levels in the main pond. However, when discharge did occur, the suspended solids levels were very low compared with other studies investigating sediment retention pond discharge.

The Richardson Road pond was less effective at removing suspended solids due to the flow regime within the forebay. There were two runoff channels entering the forebay, as well as a continual flow of groundwater. Only one of the runoff channels was directly dosed with PAC, and as the water level in the forebay was typically at, or just below, the level spreader at all times, there was a decreased potential for the PAC to become evenly distributed through the forebay and achieve dosing of all runoff. Furthermore, the main pond discharged continuously during the study period, resulting in reduced residence times of runoff within the pond system. Nonetheless, the discharge from the main pond was much lower than other studies, implying suspended solid reduction was being achieved.

The SEV method indicated that the receiving environment was already degraded due to modifications to the riparian vegetation, increased dissolved oxygen demand, and moderate bank erosion. This was reflected in the macroinvertebrate population, with only pollution tolerant taxa being collected, thus limiting the use of macroinvertebrates as an assessment tool in this study. However, the SEV method, which assesses a wide range of ecological functions, implied that very little environmental change occurred as a result of the sediment retention pond discharge. A small increase in deposited sediment was observed on the stream bed, however indications are that deposited sediment is rapidly washed away once earthworks are completed. Thus this deposited sediment may not have a permanent impact within the receiving environment.

year: 2008

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