title: Piri Pāua: Mātauranga Māori and marine science approach to growth rate and length at maturity of pāua in the Bay of Plenty 2022-2024

authors: Paul-Burke, Kura

abstract: Pāua is a highly regarded taonga (culturally important) species. Mai i ngā Kurī ā Whārei ki Tihirau (the Bay of Plenty Regional Iwi Customary Fisheries Forum) raised concerns about the current state of pāua in Te Moana-a-Toi (Bay of Plenty). This project utilised localised intergenerational observations of population dynamics in traditional harvesting areas to assess pāua productivity in the Tauranga Moana Mātaitai Reserve in Tauranga and Te Rohe Moana o Ngāti Awa in Whakatāne. The project combined mātauranga Māori alongside marine science field methods.

Results were compared with previous iwi-led research at Tauranga in 2013 and Whakatāne in 2010. The pāua population in 2023 had declined by almost half in Tauranga but remained relatively consistent across all sites in Whakatāne. However, pāua were small sized in both locations, with less than 1% of individuals reaching the Minimum Legal Size (125 mm) for harvesting. Growth rate surveys were conducted in the wild at both locations from June 2023 to the end of May 2024 and identified pāua as slow growing and, as a result, sexually mature at smaller sizes than in other regions of Aotearoa New Zealand.

text: Piri Pāua: Mātauranga Māori and marine science approach to growth rate and length at maturity of pāua in the Bay of Plenty 2022-2024 Pāua is a highly regarded taonga (culturally important) species. Mai i ngā Kurī ā Whārei ki Tihirau (the Bay of Plenty Regional Iwi Customary Fisheries Forum) raised concerns about the current state of pāua in Te Moana-a-Toi (Bay of Plenty). This project utilised localised intergenerational observations of population dynamics in traditional harvesting areas to assess pāua productivity in the Tauranga Moana Mātaitai Reserve in Tauranga and Te Rohe Moana o Ngāti Awa in Whakatāne. The project combined mātauranga Māori alongside marine science field methods.

Results were compared with previous iwi-led research at Tauranga in 2013 and Whakatāne in 2010. The pāua population in 2023 had declined by almost half in Tauranga but remained relatively consistent across all sites in Whakatāne. However, pāua were small sized in both locations, with less than 1% of individuals reaching the Minimum Legal Size (125 mm) for harvesting. Growth rate surveys were conducted in the wild at both locations from June 2023 to the end of May 2024 and identified pāua as slow growing and, as a result, sexually mature at smaller sizes than in other regions of Aotearoa New Zealand.

year: 2025

title: Can the mana of Maori men who sexually abuse children be restored?

authors: Webb, Mate

subjects: Maori

abstract: The problem of child sex abuse is prevalent across all segments of society, and Maori, unfortunately, are overrepresented in this problem. In the total prison population of 6591, 13.6% are identified as child sex offenders. Of the 3,299 Maori in prison 283 (8.5%) are identified as child sex offenders whereas 631 (18.6%) of the 3292 non-Maori have been so identified. However, Maori only represent approximately 15% of the general population. In proportionate terms, approximately one of every 970 Maori men is currently in prison for child sex offences, while for non-Maori that figure is one in 31251. Also, disclosures from offenders suggest that sexual abuse is particularly common in rural or disadvantaged areas, with offenders frequently reporting being abused by multiple offenders and being aware of chronic abuse, little of which was ever reported.

text: Can the mana of Maori men who sexually abuse children be restored? The problem of child sex abuse is prevalent across all segments of society, and Maori, unfortunately, are overrepresented in this problem. In the total prison population of 6591, 13.6% are identified as child sex offenders. Of the 3,299 Maori in prison 283 (8.5%) are identified as child sex offenders whereas 631 (18.6%) of the 3292 non-Maori have been so identified. However, Maori only represent approximately 15% of the general population. In proportionate terms, approximately one of every 970 Maori men is currently in prison for child sex offences, while for non-Maori that figure is one in 31251. Also, disclosures from offenders suggest that sexual abuse is particularly common in rural or disadvantaged areas, with offenders frequently reporting being abused by multiple offenders and being aware of chronic abuse, little of which was ever reported.

year: 2008

title: Assessing machine learning models for near-infrared regression by measuring stability towards diffeomorphisms

authors: Wohlers, Mark W.

subjects: computer science

abstract: Near infrared (NIR) spectroscopy is widely used as a tool for non-destructive assessment of fruit quality by applying measured spectra to predict quality parameters such as dry matter and soluble solids content using a suitable regression method. With continued advancements in deep learning, there is potential for improved predictive performance when neural network models are applied instead of partial least-squares regression, but choosing a model remains challenging as performance is sensitive to the model's architecture. Taking inspiration from work done in image classification, we propose model selection by assessing relative stability to diffeomorphic transformations, providing a complementary approach to standard validation methods. This is particularly useful when labelled validation data is limited. Our empirical results on several NIR regression problems indicate that the proposed approach is comparable to the use of independent validation sets. In addition to the choice of deep learning architecture, we also consider the selection of the number of components in partial least-squares regression to demonstrate the method's generality.

text: Assessing machine learning models for near-infrared regression by measuring stability towards diffeomorphisms Near infrared (NIR) spectroscopy is widely used as a tool for non-destructive assessment of fruit quality by applying measured spectra to predict quality parameters such as dry matter and soluble solids content using a suitable regression method. With continued advancements in deep learning, there is potential for improved predictive performance when neural network models are applied instead of partial least-squares regression, but choosing a model remains challenging as performance is sensitive to the model's architecture. Taking inspiration from work done in image classification, we propose model selection by assessing relative stability to diffeomorphic transformations, providing a complementary approach to standard validation methods. This is particularly useful when labelled validation data is limited. Our empirical results on several NIR regression problems indicate that the proposed approach is comparable to the use of independent validation sets. In addition to the choice of deep learning architecture, we also consider the selection of the number of components in partial least-squares regression to demonstrate the method's generality.

year: 2025