title: TDR size and quantity

authors: Park, Simon

abstract: Simon Park has been contracted by BoPRC for several years to provide advice on land use and management in the Rotorua Lakes catchment. Since 2012 this has included working with LWQS and the Farmer Collective on collaborative solutions for Lake Rotorua, especially regarding farm nutrient losses. He is the secretariat support person for the Lake Rotorua Catchment Stakeholder Advisory Group (StAG). Simon has an MSc in Earth Sciences from Waikato University. He has previously worked at MfE on sustainable land use policies and has processed resource consents at regional and district council levels.

text: TDR size and quantity Simon Park has been contracted by BoPRC for several years to provide advice on land use and management in the Rotorua Lakes catchment. Since 2012 this has included working with LWQS and the Farmer Collective on collaborative solutions for Lake Rotorua, especially regarding farm nutrient losses. He is the secretariat support person for the Lake Rotorua Catchment Stakeholder Advisory Group (StAG). Simon has an MSc in Earth Sciences from Waikato University. He has previously worked at MfE on sustainable land use policies and has processed resource consents at regional and district council levels.

year: 2013

title: Visualizing class probability estimators

authors: Frank, Eibe

subjects: Science & Technology

abstract: Inducing classifiers that make accurate predictions on future data is a driving force for research in inductive learning. However, also of importance to the users is how to gain information from the models produced. Unfortunately, some of the most powerful inductive learning algorithms generate “black boxes”—that is, the representation of the model makes it virtually impossible to gain any insight into what has been learned. This paper presents a technique that can help the user understand why a classifier makes the predictions that it does by providing a two-dimensional visualization of its class probability estimates. It requires the classifier to generate class probabilities but most practical algorithms are able to do so (or can be modified to this end).

text: Visualizing class probability estimators Inducing classifiers that make accurate predictions on future data is a driving force for research in inductive learning. However, also of importance to the users is how to gain information from the models produced. Unfortunately, some of the most powerful inductive learning algorithms generate “black boxes”—that is, the representation of the model makes it virtually impossible to gain any insight into what has been learned. This paper presents a technique that can help the user understand why a classifier makes the predictions that it does by providing a two-dimensional visualization of its class probability estimates. It requires the classifier to generate class probabilities but most practical algorithms are able to do so (or can be modified to this end).

year: 2003-01-01

title: Section 2: Native disruption: Maori and the precariat: Introduction

authors: Masters-Awatere, Bridgette

abstract: Within this section, the authors encourage the reader to look back into history and then consider the future. We are invited to re-examine deeds from this country’s past. To immerse ourselves in a narrative that may be different to the one we have been taught in school, or heard among our friends and family. Through their stories we can see multiple narratives. Readers are invited to understand, embrace and own the past. By recognising and accepting the past — our collective histories — we can determine our collective present and future.

text: Section 2: Native disruption: Maori and the precariat: Introduction Within this section, the authors encourage the reader to look back into history and then consider the future. We are invited to re-examine deeds from this country’s past. To immerse ourselves in a narrative that may be different to the one we have been taught in school, or heard among our friends and family. Through their stories we can see multiple narratives. Readers are invited to understand, embrace and own the past. By recognising and accepting the past — our collective histories — we can determine our collective present and future.

year: 2017