title: Alum dosing of Waitangi Stream: Biomonitoring of aluminium 2021-2023

authors: Tempero, Grant Wayne

abstract: Continuous alum dosing of the Waitangi Stream at Lake Rotoehu commenced in 2011 to reduce concentrations of dissolved reactive phosphorus in this inflow to the lake and improve water quality. The Bay of Plenty Regional Council contracted the University of Waikato to undertake monitoring of biota in the Waitangi Stream for potential impacts due to alum dosing. Macroinvertebrate community analysis and tissue aluminium concentrations in kōura (Paranephrops planifrons) and goldfish (Carassius auratus) have been undertaken annually since 2013 to determine the impacts and bioaccumulation of aluminium.

text: Alum dosing of Waitangi Stream: Biomonitoring of aluminium 2021-2023 Continuous alum dosing of the Waitangi Stream at Lake Rotoehu commenced in 2011 to reduce concentrations of dissolved reactive phosphorus in this inflow to the lake and improve water quality. The Bay of Plenty Regional Council contracted the University of Waikato to undertake monitoring of biota in the Waitangi Stream for potential impacts due to alum dosing. Macroinvertebrate community analysis and tissue aluminium concentrations in kōura (Paranephrops planifrons) and goldfish (Carassius auratus) have been undertaken annually since 2013 to determine the impacts and bioaccumulation of aluminium.

year: 2024

title: 'Waitangi and indigenous rights: Revolution, law and legitimacy' by F.M. Brookfield

authors: Melbourne, Hirini

subjects: indigenous rights

abstract: 'Waitangi and Indigenous Rights: Revolution, Law and Legitimacy' book review by Hirini Melbourne.

text: 'Waitangi and indigenous rights: Revolution, law and legitimacy' by F.M. Brookfield 'Waitangi and Indigenous Rights: Revolution, Law and Legitimacy' book review by Hirini Melbourne.

year: 2000-10

title: Robust mathematical programming for natural resource modelling under parametric uncertainty

authors: Doole, Graeme J.

subjects: parameter uncertainty

abstract: Inaccurate specification of model coefficients can lead to false or distorted findings in modeling investigations of natural resource management. Hence, this paper outlines a decision framework for optimization problems in which only the bounded set of outcomes for uncertain parameters is known. These models can be solved with standard mathematical programming software and are no larger than their deterministic equivalent. The robust approach is contrasted against deterministic analysis and is demonstrated for two applications regarding the management of natural resources. Deterministic plans are infeasible in at least 40% of cases when parameters vary from their point estimates. Inclusion of robust constraints immunizes against this infeasibility, thereby removing errors arising from false certainty. Additionally, incorporation of bounded parameters in the objective function yields interval-valued sets containing potential outcomes. However, this increase in the general relevance of model output introduces some degree of suboptimality as deterministic plans are buffered to proactively account for potential variability. The cost of robustness increases with the simulated spread of uncertain coefficients but may be reduced through accounting for the uncertainty aversion of decision makers.

text: Robust mathematical programming for natural resource modelling under parametric uncertainty Inaccurate specification of model coefficients can lead to false or distorted findings in modeling investigations of natural resource management. Hence, this paper outlines a decision framework for optimization problems in which only the bounded set of outcomes for uncertain parameters is known. These models can be solved with standard mathematical programming software and are no larger than their deterministic equivalent. The robust approach is contrasted against deterministic analysis and is demonstrated for two applications regarding the management of natural resources. Deterministic plans are infeasible in at least 40% of cases when parameters vary from their point estimates. Inclusion of robust constraints immunizes against this infeasibility, thereby removing errors arising from false certainty. Additionally, incorporation of bounded parameters in the objective function yields interval-valued sets containing potential outcomes. However, this increase in the general relevance of model output introduces some degree of suboptimality as deterministic plans are buffered to proactively account for potential variability. The cost of robustness increases with the simulated spread of uncertain coefficients but may be reduced through accounting for the uncertainty aversion of decision makers.

year: 2010