Journal Articles

Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915

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Subject: search.filters.subject.Fitness

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    A threshold model to determine the association between race rides and fall risk for early career (apprentice) jockeys.
    (Elsevier B.V., 2024-10-31) Legg KA; Cochrane DJ; Gee EK; Rogers CW
    Objectives To identify descriptors associated with success in apprentice jockeys and to determine optimum numbers of jockeys for safer race riding. Design Retrospective cohort study. Methods Incidence-rates for jockey falls and success (wins per 1,000 race-starts), time and number of races spent at different apprentice levels were calculated for 807 apprentice and professional jockeys over 19 years of Thoroughbred flat racing in New Zealand (n = 524,551 race-starts). Survival analysis was used to compare career progression for jockeys that fell and those that did not, and individual seasonal fall incidence-rates were modelled. Results Apprentices had the highest fall incidence-rate in their first year of race riding (2.4, interquartile range 1.7–3.2 vs 1.1, interquartile range 1.0–1.2, p < 0.05) and a lower success incidence-rate compared to non-apprentice jockeys (71, interquartile range 67–75 vs 97 interquartile range 96–98, p < 0.05). Jockeys who fell during their apprenticeship rode in more race rides to progress towards professional status than those who did not. There was an inverse power relationship between fall incidence-rate and race rides per season for jockeys, with the inflection point at 33 rides per season. Half (48 %) of the jockeys rode fewer than 33 rides per season. Conclusions There is a surplus number of jockeys, riding at high fall risk, produced than is required by the number of race riding opportunities. Greater investment into the fitness, education and selection of a smaller cohort of dedicated apprentices, may be beneficial to reduce the risk of early career fall or injury in jockeys and requires further investigation.
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    A model for phenotype change in a stochastic framework
    (American Institute of Mathematical Sciences, 2008) Wake GC
    In some species, an inducible secondary phenotype will develop some time after the environmental change that evokes it. Nishimura (2006) [4] showed how an individual organism should optimize the time it takes to respond to an environmental change ("waiting time''). If the optimal waiting time is considered to act over the population, there are implications for the expected value of the mean fitness in that population. A stochastic predator-prey model is proposed in which the prey have a fixed initial energy budget. Fitness is the product of survival probability and the energy remaining for non-defensive purposes. The model is placed in the stochastic domain by assuming that the waiting time in the population is a normally distributed random variable because of biological variance inherent in mounting the response. It is found that the value of the mean waiting time that maximises fitness depends linearly on the variance of the waiting time.