title: Fractionation of nitrogen isotopes by animals: a further complication to the use of variations in the natural abundance of ¹⁵N for tracer studies

authors: Steele, K.W.

subjects: biology

abstract: A study of the fractionation of nitrogen isotopes in the diet by cattle is described and the results discussed. Compared with the diet, urine had a lower ratio of ¹⁵N to ¹⁴N, but faeces, blood and milk all had a higher ratio. It is argued that the use of natural ¹⁵N as a tracer in grazed ecosystems is more complicated than was at first thought.

text: Fractionation of nitrogen isotopes by animals: a further complication to the use of variations in the natural abundance of ¹⁵N for tracer studies A study of the fractionation of nitrogen isotopes in the diet by cattle is described and the results discussed. Compared with the diet, urine had a lower ratio of ¹⁵N to ¹⁴N, but faeces, blood and milk all had a higher ratio. It is argued that the use of natural ¹⁵N as a tracer in grazed ecosystems is more complicated than was at first thought.

year: 1978

title: Prediction Intervals for Class Probabilities

authors: Yu, Xiaofeng

subjects: bayesian classifier

abstract: Prediction intervals for class probabilities are of interest in machine learning because they
can quantify the uncertainty about the class probability estimate for a test instance. The
idea is that all likely class probability values of the test instance are included, with a
pre-specified confidence level, in the calculated prediction interval. This thesis proposes a
probabilistic model for calculating such prediction intervals. Given the unobservability of
class probabilities, a Bayesian approach is employed to derive a complete distribution of the
class probability of a test instance based on a set of class observations of training instances
in the neighbourhood of the test instance. A random decision tree ensemble learning
algorithm is also proposed, whose prediction output constitutes the neighbourhood that
is used by the Bayesian model to produce a PI for the test instance. The Bayesian
model, which is used in conjunction with the ensemble learning algorithm and the standard
nearest-neighbour classifier, is evaluated on artificial datasets and modified real datasets.

text: Prediction Intervals for Class Probabilities Prediction intervals for class probabilities are of interest in machine learning because they
can quantify the uncertainty about the class probability estimate for a test instance. The
idea is that all likely class probability values of the test instance are included, with a
pre-specified confidence level, in the calculated prediction interval. This thesis proposes a
probabilistic model for calculating such prediction intervals. Given the unobservability of
class probabilities, a Bayesian approach is employed to derive a complete distribution of the
class probability of a test instance based on a set of class observations of training instances
in the neighbourhood of the test instance. A random decision tree ensemble learning
algorithm is also proposed, whose prediction output constitutes the neighbourhood that
is used by the Bayesian model to produce a PI for the test instance. The Bayesian
model, which is used in conjunction with the ensemble learning algorithm and the standard
nearest-neighbour classifier, is evaluated on artificial datasets and modified real datasets.

year: 2007

title: Integrating kaupapa Māori in a pākeha philosophy programme

authors: Weijers, Dan M.

abstract: Presented at the University of Waikato, Hamilton, New Zealand on 15 September 2022

text: Integrating kaupapa Māori in a pākeha philosophy programme Presented at the University of Waikato, Hamilton, New Zealand on 15 September 2022

year: 2022-09-15