title: Automatic species identification from images for Aotearoa

authors: Wang, Hongyu

subjects: computer science

abstract: Image classification for species identification has applications in areas such as conservation and education. Given New Zealand's geographic isolation and the relatively small number of species present on its islands, there is an opportunity to apply machine learning to enable accurate automatic species identification for Aotearoa, even on mobile devices without Internet access. We present neural network-based image classification models trained to classify organisms present in New Zealand. The data for model development and evaluation, obtained from the crowd-sourcing website iNaturalist, comprises 14,991 species, including 6,216 Animalia, 6,173 Plantae, and 2,407 Fungi species, alongside a small set of observations of Bacteria, Chromista, Protozoa, and Viruses. It contains organisms observed in the natural environment as well as captive and cultivated organisms. The trained models achieve over 76% classification accuracy across all species and produce class probability estimates, calibrated using temperature scaling, that can be used to gauge confidence in their classifications. Input attribution methods can be used to interpret a model's inferences by highlighting its areas of focus on images. The models are available to the public as downloadable model files and as part of both web and mobile applications for species identification that are distributed as open-source software.

text: Automatic species identification from images for Aotearoa Image classification for species identification has applications in areas such as conservation and education. Given New Zealand's geographic isolation and the relatively small number of species present on its islands, there is an opportunity to apply machine learning to enable accurate automatic species identification for Aotearoa, even on mobile devices without Internet access. We present neural network-based image classification models trained to classify organisms present in New Zealand. The data for model development and evaluation, obtained from the crowd-sourcing website iNaturalist, comprises 14,991 species, including 6,216 Animalia, 6,173 Plantae, and 2,407 Fungi species, alongside a small set of observations of Bacteria, Chromista, Protozoa, and Viruses. It contains organisms observed in the natural environment as well as captive and cultivated organisms. The trained models achieve over 76% classification accuracy across all species and produce class probability estimates, calibrated using temperature scaling, that can be used to gauge confidence in their classifications. Input attribution methods can be used to interpret a model's inferences by highlighting its areas of focus on images. The models are available to the public as downloadable model files and as part of both web and mobile applications for species identification that are distributed as open-source software.

year: 2025

title: Certification and licensing of clinical medical physicists in AFOMP countries

authors: Round, W. Howell

subjects: certification

abstract: In many countries the education and training of medical physicists has changed over the last few decades from being rather ad hoc to becoming structured with residency programs becoming quite common. These are often followed by formal certification by independent bodies, and medical physics professional organizations (MPPOs) affirming the quality of certified physicists and recognizing their competence and ability to practice independently. Policies on the training and education of medical physicists have been developed by the International Organization for Medical Physics (IOMP) and by the Asia- Oceania Federation of Organizations for Medical Physics (AFOMP). Currently nine countries in Asia-Oceania operate systems for the certification of clinical medical physicists and four others are planning or implementing such systems. The existing systems, which are described in this paper, generally conform to the policies. Licensing of medical physicists, which is primarily the responsibility of government bodies, is almost unknown in that region.

text: Certification and licensing of clinical medical physicists in AFOMP countries In many countries the education and training of medical physicists has changed over the last few decades from being rather ad hoc to becoming structured with residency programs becoming quite common. These are often followed by formal certification by independent bodies, and medical physics professional organizations (MPPOs) affirming the quality of certified physicists and recognizing their competence and ability to practice independently. Policies on the training and education of medical physicists have been developed by the International Organization for Medical Physics (IOMP) and by the Asia- Oceania Federation of Organizations for Medical Physics (AFOMP). Currently nine countries in Asia-Oceania operate systems for the certification of clinical medical physicists and four others are planning or implementing such systems. The existing systems, which are described in this paper, generally conform to the policies. Licensing of medical physicists, which is primarily the responsibility of government bodies, is almost unknown in that region.

year: 2011

title: The cost of aquatic weed control in the Rotorua Te Arawa Lakes

authors: Champion, Paul

abstract: We will present information on the costs of aquatic weed control in the Rotorua, Te Arawa Lakes and thank all of the people who have willingly given us their estimated costs. Hopefully, this provides guidance on what current activities cost and what future scenarios might be feasible.

text: The cost of aquatic weed control in the Rotorua Te Arawa Lakes We will present information on the costs of aquatic weed control in the Rotorua, Te Arawa Lakes and thank all of the people who have willingly given us their estimated costs. Hopefully, this provides guidance on what current activities cost and what future scenarios might be feasible.

year: 2017