title: Automated measurement of disease and pain in New Zealand group-housed calves

authors: Topp, Theresa

subjects: calf

abstract: Exposure to disease and pain will prolong animal ‘suffering’, and as such, diminishes welfare. Identifying behaviours indicative of these conditions can facilitate early detection, timely treatment and improved welfare. Contributing to the problem of disease and pain detection in calves is the innate tendency of these animals to mask behavioural signs of vulnerability (stoicism), and the extensive use of group-housing systems. Existing technology was used in my research to obtain objective measures of behaviour in response to disease and pain. The goal of this thesis was to explore these key aspects of calf welfare:
The work reported in Chapter 2 investigated a naturally occurring disease (neonatal calf diarrhoea complex) in pre-weaned calves to assess whether changes in milk-feeding and lying behaviours could be used for early disease detection. Calves were observed for a three week period, starting when they were four days old. Data on milk feeding and lying behaviours were obtained using automated milk-feeders and HOBO data loggers respectively. Lying postures were analysed from daily video footage at five minute intervals between 10:00-14:00 (seven days per week). For statistical analysis, calves were classified as ‘sick’ (n=21), or ‘not sick’ (n=91). This thesis identified three feeding measures of interest for disease detection: 1) reduced milk consumption, 2) increased duration of visits to the milk feeder, and 3) sick calves were less likely to receive a rewarded visit compared to calves that were not sick. Sick calves increased the duration of lying bouts nearing time of illness; however, no difference was observed between sick calves and those that were not sick. Postural observations were not effective at predicting illness. The use of automated milk-feeders to detect disease in calves has been studied extensively overseas; to my knowledge, this is the first New Zealand study to use automated feeders for this purpose. The results of this study indicate that aspects of milk feeding behaviour can be used to detect diseased calves in group housing systems.         
The work reported in Chapter 3 used hot iron disbudding as a pain model to determine whether changes in milk-feeding and lying behaviours could be used to identify pain in calves less than 4 weeks of age. Data was obtained using automated feeders and HOBO data loggers respectively over three observation periods (pre-treatment, treatment day and post-treatment). Fifty-three calves (26.5 ± 3.5 days of age) were allocated to one of five treatment groups: hot iron disbudded with no analgesia (n=11), disbudded with a local anaesthetic (LA, n=11), disbudded with a non-steroidal anti-inflammatory drug (NSAID; n=11), disbudded with NSAID and LA (n=10), and SHAM calves (n=11). Analysis of feeding behaviour revealed only one difference between all treatment groups; SHAM calves showed a greater number of visits to the milk feeder during the recovery period compared to disbudded animals. Feeding and lying behaviours in this study were considered to be insufficient measures of pain. The use of automated milk-feeders to detect pain in calves is limited, thus necessitating this research.

text: Automated measurement of disease and pain in New Zealand group-housed calves Exposure to disease and pain will prolong animal ‘suffering’, and as such, diminishes welfare. Identifying behaviours indicative of these conditions can facilitate early detection, timely treatment and improved welfare. Contributing to the problem of disease and pain detection in calves is the innate tendency of these animals to mask behavioural signs of vulnerability (stoicism), and the extensive use of group-housing systems. Existing technology was used in my research to obtain objective measures of behaviour in response to disease and pain. The goal of this thesis was to explore these key aspects of calf welfare:
The work reported in Chapter 2 investigated a naturally occurring disease (neonatal calf diarrhoea complex) in pre-weaned calves to assess whether changes in milk-feeding and lying behaviours could be used for early disease detection. Calves were observed for a three week period, starting when they were four days old. Data on milk feeding and lying behaviours were obtained using automated milk-feeders and HOBO data loggers respectively. Lying postures were analysed from daily video footage at five minute intervals between 10:00-14:00 (seven days per week). For statistical analysis, calves were classified as ‘sick’ (n=21), or ‘not sick’ (n=91). This thesis identified three feeding measures of interest for disease detection: 1) reduced milk consumption, 2) increased duration of visits to the milk feeder, and 3) sick calves were less likely to receive a rewarded visit compared to calves that were not sick. Sick calves increased the duration of lying bouts nearing time of illness; however, no difference was observed between sick calves and those that were not sick. Postural observations were not effective at predicting illness. The use of automated milk-feeders to detect disease in calves has been studied extensively overseas; to my knowledge, this is the first New Zealand study to use automated feeders for this purpose. The results of this study indicate that aspects of milk feeding behaviour can be used to detect diseased calves in group housing systems.         
The work reported in Chapter 3 used hot iron disbudding as a pain model to determine whether changes in milk-feeding and lying behaviours could be used to identify pain in calves less than 4 weeks of age. Data was obtained using automated feeders and HOBO data loggers respectively over three observation periods (pre-treatment, treatment day and post-treatment). Fifty-three calves (26.5 ± 3.5 days of age) were allocated to one of five treatment groups: hot iron disbudded with no analgesia (n=11), disbudded with a local anaesthetic (LA, n=11), disbudded with a non-steroidal anti-inflammatory drug (NSAID; n=11), disbudded with NSAID and LA (n=10), and SHAM calves (n=11). Analysis of feeding behaviour revealed only one difference between all treatment groups; SHAM calves showed a greater number of visits to the milk feeder during the recovery period compared to disbudded animals. Feeding and lying behaviours in this study were considered to be insufficient measures of pain. The use of automated milk-feeders to detect pain in calves is limited, thus necessitating this research.

year: 2015

title: Revisiting deep hybrid models for out-of-distribution detection

authors: Schlumbom, Paul-Ruben

subjects: computer science

abstract: Deep hybrid models (DHMs) for out-of-distribution (OOD) detection, jointly training a deep feature extractor with a classification head and a density estimation head based on a normalising flow, provide a conceptually appealing approach to visual OOD detection. The paper that introduced this approach reported 100% AuROC in experiments on two standard benchmarks, including one based on the CIFAR-10 data. As there are no implementations available, we set out to reproduce the approach by carefully filling in gaps in the description of the algorithm. Although we were unable to attain 100% OOD detection rates, and our results indicate that such performance is impossible on the CIFAR-10 benchmark, we achieved good OOD performance. We provide a detailed analysis of when the architecture fails and argue that it introduces an adversarial relationship between the classification component and the density estimator, rendering it highly sensitive to the balance of these two components and yielding a collapsed feature space without careful fine-tuning. Our implementation of DHMs is publicly available.

text: Revisiting deep hybrid models for out-of-distribution detection Deep hybrid models (DHMs) for out-of-distribution (OOD) detection, jointly training a deep feature extractor with a classification head and a density estimation head based on a normalising flow, provide a conceptually appealing approach to visual OOD detection. The paper that introduced this approach reported 100% AuROC in experiments on two standard benchmarks, including one based on the CIFAR-10 data. As there are no implementations available, we set out to reproduce the approach by carefully filling in gaps in the description of the algorithm. Although we were unable to attain 100% OOD detection rates, and our results indicate that such performance is impossible on the CIFAR-10 benchmark, we achieved good OOD performance. We provide a detailed analysis of when the architecture fails and argue that it introduces an adversarial relationship between the classification component and the density estimator, rendering it highly sensitive to the balance of these two components and yielding a collapsed feature space without careful fine-tuning. Our implementation of DHMs is publicly available.

year: 2025

title: Māori & Psychology Research Unit annual report 2010

authors: Rua, Mohi

subjects: Māori and Psychology Research Unit

abstract: Annual report of the Māori and Psychology Research Unit (MPRU) 2010. The unit was established in August of 1997. The unit is designed to provide a catalyst and support network for enhancing research concerning the psychological needs, aspirations, and priorities of Maori people. The MPRU is well situated to draw together skilled and experienced interdisciplinary research groups by networking and establishing working relationships with staff and students within the Faculty of Arts and Social Sciences, the University, and the wider community.

text: Māori & Psychology Research Unit annual report 2010 Annual report of the Māori and Psychology Research Unit (MPRU) 2010. The unit was established in August of 1997. The unit is designed to provide a catalyst and support network for enhancing research concerning the psychological needs, aspirations, and priorities of Maori people. The MPRU is well situated to draw together skilled and experienced interdisciplinary research groups by networking and establishing working relationships with staff and students within the Faculty of Arts and Social Sciences, the University, and the wider community.

year: 2011