title: Temporal and spatial variations in phytoplankton productivity in surface waters of a warm-temperate, monomictic lake in New Zealand

authors: von Westernhagen, Nina

subjects: Eutrophication

abstract: Surface phytoplankton productivity measurements were carried out in morphologically complex Lake Rotoiti with the objective of defining variations between sites and seasons, and the dominant environmental drivers of these variations. Measurements were carried out monthly at two depths at each of three morphologically diverse stations for 1 year throughout the lake. Productivity at the surface of the shallow embayment was significantly higher in most months of the year compared with the surface of the other two stations but there were no significant differences from September to December 2004. There were no relationships between measured environmental variables and primary productivity or specific production. Inorganic nutrient concentrations at the surface of the shallow station were low throughout the whole year but at the other two stations they showed a typical pattern for monomictic lakes of higher levels during winter mixing and declining concentrations during thermal stratification. The high variability between sites found in this study indicates that it is important to account for local differences in productivity in morphologically diverse lakes, and that whole lake productivity estimates may vary greatly depending on the location and depth of productivity measurements.

text: Temporal and spatial variations in phytoplankton productivity in surface waters of a warm-temperate, monomictic lake in New Zealand Surface phytoplankton productivity measurements were carried out in morphologically complex Lake Rotoiti with the objective of defining variations between sites and seasons, and the dominant environmental drivers of these variations. Measurements were carried out monthly at two depths at each of three morphologically diverse stations for 1 year throughout the lake. Productivity at the surface of the shallow embayment was significantly higher in most months of the year compared with the surface of the other two stations but there were no significant differences from September to December 2004. There were no relationships between measured environmental variables and primary productivity or specific production. Inorganic nutrient concentrations at the surface of the shallow station were low throughout the whole year but at the other two stations they showed a typical pattern for monomictic lakes of higher levels during winter mixing and declining concentrations during thermal stratification. The high variability between sites found in this study indicates that it is important to account for local differences in productivity in morphologically diverse lakes, and that whole lake productivity estimates may vary greatly depending on the location and depth of productivity measurements.

year: 2010

title: Mining data streams using option trees (revised edition, 2004)

authors: Holmes, Geoffrey

subjects: Machine learning

abstract: The data stream model for data mining places harsh restrictions on a learning algorithm. A model must be induced following the briefest interrogation of the data, must use only available memory and must update itself over time within these constraints. Additionally, the model must be able to be used for data mining at any point in time.
This paper describes a data stream classi_cation algorithm using an ensemble of option trees. The ensemble of trees is induced by boosting and iteratively combined into a single interpretable model. The algorithm is evaluated using benchmark datasets for accuracy against state-of-the-art algorithms that make use of the entire dataset.

text: Mining data streams using option trees (revised edition, 2004) The data stream model for data mining places harsh restrictions on a learning algorithm. A model must be induced following the briefest interrogation of the data, must use only available memory and must update itself over time within these constraints. Additionally, the model must be able to be used for data mining at any point in time.
This paper describes a data stream classi_cation algorithm using an ensemble of option trees. The ensemble of trees is induced by boosting and iteratively combined into a single interpretable model. The algorithm is evaluated using benchmark datasets for accuracy against state-of-the-art algorithms that make use of the entire dataset.

year: 2004-01-01

title: “Seeing” Kāpō Māori: Making visible the experiences of Kāpō Māori during and after COVID-19

authors: Masters-Awatere, Bridgette

abstract: A scoping project focused on the intersection of Māori and the framing of vision impairments (disability) within the context of a pandemic response (in this instance COVID-19). Specifically, we were interested in the experiences of kāpō Māori during and post COVID-19 in order to centre the cultural and health needs of kāpō Māori in a culturally responsive manner. We highlight the aspirations that kāpō Māori have for themselves and their whānau, and to identify the facilitators and barriers that kāpō Māori and their whānau face in achieving these

text: “Seeing” Kāpō Māori: Making visible the experiences of Kāpō Māori during and after COVID-19 A scoping project focused on the intersection of Māori and the framing of vision impairments (disability) within the context of a pandemic response (in this instance COVID-19). Specifically, we were interested in the experiences of kāpō Māori during and post COVID-19 in order to centre the cultural and health needs of kāpō Māori in a culturally responsive manner. We highlight the aspirations that kāpō Māori have for themselves and their whānau, and to identify the facilitators and barriers that kāpō Māori and their whānau face in achieving these

year: 2021