title: Aluminium content in near-shore sediments of Lake Rotorua

authors: Tempero, Grant Wayne

abstract: The application of alum (aluminium sulphate) is widely used for lake restoration. Aluminium binds dissolved reactive phosphate (DRP), reducing its availability to phytoplankton and inhibiting algal blooms. The Bay of Plenty Regional Council has conducted continuous alum dosing of the Utuhina Stream since 2006 and the Puarenga Stream from 2010 to 2018. This has resulted in the addition of more than 730 tonnes of aluminium to Lake Rotorua. Improvements in Lake Rotorua water quality occurred concurrently with peak alum dosing, although sediment surveys conducted in 2012 and 2015 were unable to locate significant depositional areas of aluminium floc in the main basin of Lake Rotorua.

Elevated amorphous aluminium (i.e., recently deposited aluminium) was reported at sites closest to the Utuhina and Puarenga discharge points in the 2015 sediment survey, indicating that aluminium was potentially accumulating in the nearshore zone. The University of Waikato was contracted by the Bay of Plenty Regional Council to conduct a follow-up survey of near-shore sites along the southern edge of Lake Rotorua. In addition to sediment total and amorphous aluminium content, total phosphorus content was also determined. Amorphous aluminium is recognised as the fraction of total aluminium able to adsorb dissolved phosphorus, sequestering it from the water column. For this study it was assumed that increased proportions of amorphous aluminium were primarily derived from alum dosing.

text: Aluminium content in near-shore sediments of Lake Rotorua The application of alum (aluminium sulphate) is widely used for lake restoration. Aluminium binds dissolved reactive phosphate (DRP), reducing its availability to phytoplankton and inhibiting algal blooms. The Bay of Plenty Regional Council has conducted continuous alum dosing of the Utuhina Stream since 2006 and the Puarenga Stream from 2010 to 2018. This has resulted in the addition of more than 730 tonnes of aluminium to Lake Rotorua. Improvements in Lake Rotorua water quality occurred concurrently with peak alum dosing, although sediment surveys conducted in 2012 and 2015 were unable to locate significant depositional areas of aluminium floc in the main basin of Lake Rotorua.

Elevated amorphous aluminium (i.e., recently deposited aluminium) was reported at sites closest to the Utuhina and Puarenga discharge points in the 2015 sediment survey, indicating that aluminium was potentially accumulating in the nearshore zone. The University of Waikato was contracted by the Bay of Plenty Regional Council to conduct a follow-up survey of near-shore sites along the southern edge of Lake Rotorua. In addition to sediment total and amorphous aluminium content, total phosphorus content was also determined. Amorphous aluminium is recognised as the fraction of total aluminium able to adsorb dissolved phosphorus, sequestering it from the water column. For this study it was assumed that increased proportions of amorphous aluminium were primarily derived from alum dosing.

year: 2019

title: Interpretable deep learning for surgical tool management

authors: Rodrigues, Mark William

subjects: computer science

abstract: This paper presents a novel convolutional neural network framework for multi-level classification of surgical tools. Our classifications are obtained from multiple levels of the model, and high accuracy is obtained by adjusting the depth of layers selected for predictions. Our framework enhances the interpretability of the overall predictions by providing a comprehensive set of classifications for each tool. This allows users to make rational decisions about whether to trust the model based on multiple pieces of information, and the predictions can be evaluated against each other for consistency and error-checking. The multi-level prediction framework achieves promising results on a novel surgery tool dataset and surgery knowledge base, which are important contributions of our work. This framework provides a viable solution for intelligent management of surgical tools in a hospital, potentially leading to significant cost savings and increased efficiencies.

text: Interpretable deep learning for surgical tool management This paper presents a novel convolutional neural network framework for multi-level classification of surgical tools. Our classifications are obtained from multiple levels of the model, and high accuracy is obtained by adjusting the depth of layers selected for predictions. Our framework enhances the interpretability of the overall predictions by providing a comprehensive set of classifications for each tool. This allows users to make rational decisions about whether to trust the model based on multiple pieces of information, and the predictions can be evaluated against each other for consistency and error-checking. The multi-level prediction framework achieves promising results on a novel surgery tool dataset and surgery knowledge base, which are important contributions of our work. This framework provides a viable solution for intelligent management of surgical tools in a hospital, potentially leading to significant cost savings and increased efficiencies.

year: 2021

title: Virtual hākari:  Sharing Māori culinary traditions in online communities

authors: Muru-Lanning, Charlotte

subjects: Kai Māori

abstract: For Māori, kai is a vital expression of identity, community, place and whakapapa. Traditionally, knowledge about kai is transmitted through customary community groupings and culturally embedded practices. However, with the rise of digital platforms, new spaces have emerged for Māori to gather and to share kai in virtual forms. 
This thesis observes how two Facebook groups – Kai Maori and KAI Basket NZ – operate as contemporary spaces for the transmission of Māori culinary knowledge and cultural identity. Kai Māori is considered alongside global studies of culinary traditions and understood through the notion of kai as whakapapa. The thesis addresses two core research questions: 1) How do Facebook groups contribute to an evolving concept of kai Māori? and 2) What kinds of relationships define these online kai communities? 
Guided by Māori data sovereignty (MDSov) principles, I observed interactions on both Facebook pages in two phases – one involving selective data collection over a twelve-month period from January to December 2024 the other involving systematic data collection over a four-week period from 10 December 2024 to 10 January 2025. The observation periods yielded 115 and 1293 posts respectively. A covert digital approach of ‘lurking’ was adopted as seeking prior consent may have affected the quality of the data and disrupted group dynamics. Lurking, as a method, sits uneasily alongside MDSov principles, particularly the principle of manaakitanga, requiring free, prior and informed consent. To address this I safeguarded the confidentiality of community members by seeking consent before reproducing content, removing group members’ names and excluding sensitive information. To analyse posts I used Ethnographic Content Analysis which allowed me to conduct a reflexive, iterative analysis of the posts in a way that was sensitive to the context in which the content was created.
These digital observations show that these groups serve as platforms for the transmission and innovation of kai Māori, encompassing edible components as well as the values enmeshed within these culinary traditions. Despite the mediated nature of these exchanges – where food is virtually consumed rather than physically shared, separate from traditional notions of community – these groups nurture meaningful connections among members, including those physically distant from their whenua. Through recipes, photos, stories, and commentary, the members of Kai Maori and KAI Basket NZ cultivate community identity and belonging via common cultural understandings, sharing of knowledge, a general spirit of sociality – and, importantly, a mutual love of kai.
I conceptualise these online spaces as virtual hākari, where the act of sharing virtual kai becomes an affirmation of identity and a form of cultural resistance. While online sharing may risk distancing kai practices and knowledge from traditional sites of transmission, the data suggests that these platforms may serve as conduits that encourage deeper cultural engagement offline. This thesis highlights the centrality of food in Māori cultural survival, sovereignty and resistance. In these digital spaces, kai Māori is not only celebrated but continually reimagined – reinforcing that to prepare, share and celebrate kai as Māori is to actively participate in the continuity of cultural identity.

text: Virtual hākari:  Sharing Māori culinary traditions in online communities For Māori, kai is a vital expression of identity, community, place and whakapapa. Traditionally, knowledge about kai is transmitted through customary community groupings and culturally embedded practices. However, with the rise of digital platforms, new spaces have emerged for Māori to gather and to share kai in virtual forms. 
This thesis observes how two Facebook groups – Kai Maori and KAI Basket NZ – operate as contemporary spaces for the transmission of Māori culinary knowledge and cultural identity. Kai Māori is considered alongside global studies of culinary traditions and understood through the notion of kai as whakapapa. The thesis addresses two core research questions: 1) How do Facebook groups contribute to an evolving concept of kai Māori? and 2) What kinds of relationships define these online kai communities? 
Guided by Māori data sovereignty (MDSov) principles, I observed interactions on both Facebook pages in two phases – one involving selective data collection over a twelve-month period from January to December 2024 the other involving systematic data collection over a four-week period from 10 December 2024 to 10 January 2025. The observation periods yielded 115 and 1293 posts respectively. A covert digital approach of ‘lurking’ was adopted as seeking prior consent may have affected the quality of the data and disrupted group dynamics. Lurking, as a method, sits uneasily alongside MDSov principles, particularly the principle of manaakitanga, requiring free, prior and informed consent. To address this I safeguarded the confidentiality of community members by seeking consent before reproducing content, removing group members’ names and excluding sensitive information. To analyse posts I used Ethnographic Content Analysis which allowed me to conduct a reflexive, iterative analysis of the posts in a way that was sensitive to the context in which the content was created.
These digital observations show that these groups serve as platforms for the transmission and innovation of kai Māori, encompassing edible components as well as the values enmeshed within these culinary traditions. Despite the mediated nature of these exchanges – where food is virtually consumed rather than physically shared, separate from traditional notions of community – these groups nurture meaningful connections among members, including those physically distant from their whenua. Through recipes, photos, stories, and commentary, the members of Kai Maori and KAI Basket NZ cultivate community identity and belonging via common cultural understandings, sharing of knowledge, a general spirit of sociality – and, importantly, a mutual love of kai.
I conceptualise these online spaces as virtual hākari, where the act of sharing virtual kai becomes an affirmation of identity and a form of cultural resistance. While online sharing may risk distancing kai practices and knowledge from traditional sites of transmission, the data suggests that these platforms may serve as conduits that encourage deeper cultural engagement offline. This thesis highlights the centrality of food in Māori cultural survival, sovereignty and resistance. In these digital spaces, kai Māori is not only celebrated but continually reimagined – reinforcing that to prepare, share and celebrate kai as Māori is to actively participate in the continuity of cultural identity.

year: 2025