title: Quantitative mineral mapping of drill core surfaces II: long-wave infrared mineral characterization using μXRF and machine learning

authors: Barker, Rocky D.

subjects: computer science

abstract: Long-wave infrared (LWIR) spectra can be interpreted using a Random Forest machine learning approach to predict mineral species and abundances. In this study, hydrothermally altered carbonate rock core samples from the Fourmile Carlin-type Au discovery, Nevada, were analyzed by LWIR and micro-X-ray fluorescence (μXRF). Linear programming-derived mineral abundances from quantified μXRF data were used as training data to construct a series of Random Forest regression models. The LWIR Random Forest models produced mineral proportion estimates with root mean square errors of 1.17 to 6.75% (model predictions) and 1.06 to 6.19% (compared to quantitative X-ray diffraction data) for calcite, dolomite, kaolinite, white mica, phlogopite, K-feldspar, and quartz. These results are comparable to the error of proportion estimates from linear spectral deconvolution (±7–15%), a commonly used spectral unmixing technique. Having a mineralogical and chemical training data set makes it possible to identify and quantify mineralogy and provides a more robust and meaningful LWIR spectral interpretation than current methods of utilizing a spectral library or spectral end-member extraction. Using the method presented here, LWIR spectroscopy can be used to overcome the limitations inherent with the use of short-wave infrared (SWIR) in fine-grained, low reflectance rocks. This new approach can be applied to any deposit type, improving the accuracy and speed of infrared data interpretation.

text: Quantitative mineral mapping of drill core surfaces II: long-wave infrared mineral characterization using μXRF and machine learning Long-wave infrared (LWIR) spectra can be interpreted using a Random Forest machine learning approach to predict mineral species and abundances. In this study, hydrothermally altered carbonate rock core samples from the Fourmile Carlin-type Au discovery, Nevada, were analyzed by LWIR and micro-X-ray fluorescence (μXRF). Linear programming-derived mineral abundances from quantified μXRF data were used as training data to construct a series of Random Forest regression models. The LWIR Random Forest models produced mineral proportion estimates with root mean square errors of 1.17 to 6.75% (model predictions) and 1.06 to 6.19% (compared to quantitative X-ray diffraction data) for calcite, dolomite, kaolinite, white mica, phlogopite, K-feldspar, and quartz. These results are comparable to the error of proportion estimates from linear spectral deconvolution (±7–15%), a commonly used spectral unmixing technique. Having a mineralogical and chemical training data set makes it possible to identify and quantify mineralogy and provides a more robust and meaningful LWIR spectral interpretation than current methods of utilizing a spectral library or spectral end-member extraction. Using the method presented here, LWIR spectroscopy can be used to overcome the limitations inherent with the use of short-wave infrared (SWIR) in fine-grained, low reflectance rocks. This new approach can be applied to any deposit type, improving the accuracy and speed of infrared data interpretation.

year: 2020

title: A farmer's perspective

authors: Morrison, Stuart

abstract: Stuart’s family came to farm in the Rotorua area in 1965. After a time away, including pursuing a career in industry, he returned. He has been dairy farming in the Lake Rotorua catchment for more than 30 years. He has been interested in, and part of the discussions about the lake and its catchment for a lot of that time, more particularly in recent years.

text: A farmer's perspective Stuart’s family came to farm in the Rotorua area in 1965. After a time away, including pursuing a career in industry, he returned. He has been dairy farming in the Lake Rotorua catchment for more than 30 years. He has been interested in, and part of the discussions about the lake and its catchment for a lot of that time, more particularly in recent years.

year: 2013

title: Mahi Toi Skills in Contemporary Education, Training and Maori Development

authors: Joseph, Makarita Ngapine Tangitu

subjects: Kia mau ki nga taonga tuku iho o nga tupuna

abstract: Since the 1980’s, national investment in human capital and skill formation policy in education has been promoted as key to economic success and social inclusion. Social inclusion, however, is often expressed as helping citizens to become employable. Although employability is a key focus of skills formation and education policy, the context of the western ideologies that it derives from and caters to perpetuates the exclusion of mahi toi (arts and crafts) skills in education. Using a kaupapa Māori approach, two key informants who specialise and teach mahi toi skills, either rāranga (weaving) or pounamu (jade/greenstone), were interviewed. The aim was to get a better understanding of how these skills can contribute to personal, professional and Māori development in the 21st century, and to provide information indicating where and how these skills can be learnt, either through private training enterprises (PTE) or public tertiary educational institutions (TEI), thereby highlighting the value of teaching and learning rāranga and pounamu skills. Findings from analysing their pūrākau indicate that learning rāranga or pounamu skills can provide a foundation for believing in your potential, contribute to cultural identity, improve confidence and communication skills, help an individual be bestowed or acquire mana, offer a type of therapy, enhance career progression and professional identity, provide financial rewards and stability, and help with the revitalisation and transfer of cultural knowledge. Ultimately, it is concluded that mahi toi skills such as rāranga and pounamu can contribute to the development of an individual’s personal and professional lives, but also has benefits for the organisation where they become employed as well as extended whānau, hapū and iwi.

text: Mahi Toi Skills in Contemporary Education, Training and Maori Development Since the 1980’s, national investment in human capital and skill formation policy in education has been promoted as key to economic success and social inclusion. Social inclusion, however, is often expressed as helping citizens to become employable. Although employability is a key focus of skills formation and education policy, the context of the western ideologies that it derives from and caters to perpetuates the exclusion of mahi toi (arts and crafts) skills in education. Using a kaupapa Māori approach, two key informants who specialise and teach mahi toi skills, either rāranga (weaving) or pounamu (jade/greenstone), were interviewed. The aim was to get a better understanding of how these skills can contribute to personal, professional and Māori development in the 21st century, and to provide information indicating where and how these skills can be learnt, either through private training enterprises (PTE) or public tertiary educational institutions (TEI), thereby highlighting the value of teaching and learning rāranga and pounamu skills. Findings from analysing their pūrākau indicate that learning rāranga or pounamu skills can provide a foundation for believing in your potential, contribute to cultural identity, improve confidence and communication skills, help an individual be bestowed or acquire mana, offer a type of therapy, enhance career progression and professional identity, provide financial rewards and stability, and help with the revitalisation and transfer of cultural knowledge. Ultimately, it is concluded that mahi toi skills such as rāranga and pounamu can contribute to the development of an individual’s personal and professional lives, but also has benefits for the organisation where they become employed as well as extended whānau, hapū and iwi.

year: 2018