In our analysis, K-means was implemented using sci-kit-learn library with a maximum iteration of 500 and a threshold of <img src="/na101/home/literatum/publisher/tandf/journals/content/ktmp20/2022/ktmp20.v009.i04/23328940.2022.2086777/20221101/images/ktmp_a_2086777_ilm0001.gif" alt="" />1×10−41×10−4<math><mn>1</mn><mo>×</mo><mrow><msup><mn>10</mn><mrow><mo>−</mo><mn>4</mn></mrow></msup></mrow></math>. Variables used for K-means were the runners’ marathon net timings and the Tdb and Twb experienced by the runners. Since K-means depends on Euclidean distance to perform clustering, it is critical that variables are of the same order of magnitude to ensure that clustering does not rely on variables with larger orders of magnitude. As the runners’ marathon net timings are a few orders of magnitude larger than Tdb and Twb, all variables were standardized to range from 0 and 1 using the Min-Max Scaler prior to K-means algorithm implementation as follows (Equation 1): <img src="/na101/home/literatum/publisher/tandf/journals/content/ktmp20/2022/ktmp20.v009.i04/23328940.2022.2086777/20221101/images/ktmp_a_2086777_m0001.gif" alt="" />(1) Z=X−min(X)max(X)−min(X)Z=X−minXmaxX−minX<math><mi>Z</mi><mo>=</mo><mrow><mfrac><mrow><mi>X</mi><mo>−</mo><mo form="prefix">min</mo><mfenced open="(" close=")"><mi>X</mi></mfenced></mrow><mrow><mo movablelimits="true">max</mo><mfenced open="(" close=")"><mi>X</mi></mfenced><mo>−</mo><mo form="prefix" movablelimits="true">min</mo><mfenced open="(" close=")"><mi>X</mi></mfenced></mrow></mfrac></mrow></math>(1) where Z is the scaled value based on the Min-Max Scaler and X is the original value of the variable.

illustrate the regression coefficients for Twb for K-means clustering-filtered male and female runners. The regression coefficients for Twb for male runners (between 34.33 and 43.41) and female runners (between 27.58 and 53.86) across all performance-level groups were similar (overlap in 95% confidence intervals for male and female runners). For Tdb, regression coefficients across gender and across all performance groups were similar (overlap in 95% confidence intervals). The findings generated by the regression analysis for Twb for all percentile-filtered male and female runners were similar to that of Figure 6, with the exception of the regression coefficients for female LMP and HMP runners being less conclusive (p > 0.025). The regression coefficients for Twb for male runners for LMP, HMP and across all performance groups were 47.34, 28.40, and 38.87, respectively, while the regression coefficient for Twb for female runners across all performance groups was 40.72. As such, the influence of Twb on net time across genders was still observed across all performance groups; however, the regression coefficients for Twb for female LMP and HMP runners were less conclusive (p > 0.025). Small changes in thermal conditions hinder marathon running performance in the tropicsAll authorsGlenn C. W. Tan , Kaiyuan Zheng, Wee K. Cheong, Christopher Byrne, Jan N. Iversen & Jason K. W. Lee https://doi.org/10.1080/23328940.2022.2086777Published online:15 July 2022

Figure 1 illustrates the age and sex distribution of runners in 2017. As of 2017 for female runners, 7 were aged 20–29, 14 were aged 30–39, 32 were aged 40–49 and 10 were aged 50–59. Likewise for male runners, 30 were aged 20–29, 138 were aged 30–39, 208 were aged 40–49, 152 were aged 50–59 and 19 were aged 60–69. The 2018 and 2019 distributions also consisted of the same 63 female runners and 547 male runners.

In our analysis, K-means was implemented using sci-kit-learn library with a maximum iteration of 500 and a threshold of <img src="/na101/home/literatum/publisher/tandf/journals/content/ktmp20/2022/ktmp20.v009.i04/23328940.2022.2086777/20221101/images/ktmp_a_2086777_ilm0001.gif" alt="" />1×10−41×10−4<math><mn>1</mn><mo>×</mo><mrow><msup><mn>10</mn><mrow><mo>−</mo><mn>4</mn></mrow></msup></mrow></math>. Variables used for K-means were the runners’ marathon net timings and the Tdb and Twb experienced by the runners. Since K-means depends on Euclidean distance to perform clustering, it is critical that variables are of the same order of magnitude to ensure that clustering does not rely on variables with larger orders of magnitude. As the runners’ marathon net timings are a few orders of magnitude larger than Tdb and Twb, all variables were standardized to range from 0 and 1 using the Min-Max Scaler prior to K-means algorithm implementation as follows (Equation 1): <img src="/na101/home/literatum/publisher/tandf/journals/content/ktmp20/2022/ktmp20.v009.i04/23328940.2022.2086777/20221101/images/ktmp_a_2086777_m0001.gif" alt="" />(1) Z=X−min(X)max(X)−min(X)Z=X−minXmaxX−minX<math><mi>Z</mi><mo>=</mo><mrow><mfrac><mrow><mi>X</mi><mo>−</mo><mo form="prefix">min</mo><mfenced open="(" close=")"><mi>X</mi></mfenced></mrow><mrow><mo movablelimits="true">max</mo><mfenced open="(" close=")"><mi>X</mi></mfenced><mo>−</mo><mo form="prefix" movablelimits="true">min</mo><mfenced open="(" close=")"><mi>X</mi></mfenced></mrow></mfrac></mrow></math>(1)

Arguments Richard Harrison, executive director of Floridians For A Sensible Voting Rights Policy, said:[48] “ Other than murder and sexual felonies, it [the initiative] treats all other felonies as though they were the same. It's a blanket, automatic restoration of voting rights. If it gets on the ballot, your only choice will be an all or nothing, yes or no vote on the amendment. If it passes, neither you nor anyone else will ever be allowed to consider the specifics of the crime or the post-release history of the criminal before that new voter registration card is issued.[17] ” Paul Wright, founder and executive director of the Human Rights Defense Center, a nonprofit based in Lake Worth that advocates for progressive criminal justice reform, wrote the following in the Tallahassee Democrat:[49] “ The problem with Amendment 4 is that it perpetuates the discrimination and bigotry of disenfranchisement against a subclass of ex-felons – those convicted of murder or sex crimes. If Amendment 4 passes, it will enshrine into our state constitution discrimination against convicted murderers and sex offenders that will make enfranchising them virtually impossible. While some may point to the serious nature of their offenses, they have nothing to do with voting. The punishment of disenfranchisement does not fit the crime. I was convicted of murder in Washington State in 1987 for killing a drug dealer during an armed robbery. In 1990, while serving a 25-year sentence, I started a nonprofit magazine from my prison cell which today employs 18 people to advocate for just, humane and fair criminal justice policies. I pay taxes, work to improve my community and am a productive member of society. But the backers of Amendment 4 would deny me the right to vote.[17] ”

The Brennan Center and other civil rights groups filed a lawsuit in federal court challenging the law, and our suit was consolidated with similar cases filed by others.