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Abstract:Measuring soil organic carbon (SOC) in riparian forest soils affected by floods is crucial for evaluating their concentration and distribution along hydrological gradients (longitudinal and transversal). Hydromorphological factors (e.g., sedimentation vs. erosion, size of floodplain, flood recurrence) may be the cause of major variations in the concentration of organic matter and SOC in soils and could have a direct impact on C levels in soil profiles. For this study, SOC concentrations were assessed in riparian soils collected along transects perpendicular to the riverbanks which cross through inundated and non-inundated zones. Other soil properties (e.g., acidity, nitrogen, texture, bulk density) that may affect the concentration of SOC were also considered. The main purpose of this study was to assess SOC concentrations in soils subjected to flooding with those outside the flood zones, and also measure various soil properties (in surface soils and at various depths ranging from 0 to 100 cm) for each selected area. Across the various areas, SOC shows marked differences in concentration and spatial distribution, with the lowest values found in mineral soils affected by successive floods (recurrence of 0-20 years). SOC at 0-20 cm in depth was significantly lower in active floodplains (Tukey HSD test), with average values of 2.29 ± 1.64% compared to non-inundated soils (3.83 ± 2.22%). The proportion of C stocks calculated in soils (inundated vs. non-inundated zones) was significantly different, with average values of 38.22 ± 10.40 and 79.75 ± 29.47 t·ha −1 , respectively. Flood frequency appears to be a key factor in understanding the low SOC concentrations in floodplain soils subjected to high flood recurrence (0-20 years).

Abstract:The predictive performance of various team metrics is compared in the context of 105 best-of-seven national hockey league (NHL) playoff series that took place between 2008 and 2014 inclusively. This analysis provides renewed support for traditional box score statistics such as goal differential, especially in the form of Pythagorean expectations. A parsimonious relevance vector machine (RVM) learning approach is compared with the more common support vector machine (SVM) algorithm. Despite the potential of the RVM approach, the SVM algorithm proved to be superior in the context of hockey playoffs. The probabilistic SVM results are used to derive playoff performance expectations for NHL teams and identify playoff under-achievers and over-achievers. The results suggest that the Arizona Coyotes and the Carolina Hurricanes can both be considered Round 2 over-achievers while the Nashville Predators would be Round 2 under-achievers, even after accounting for several observable team performance metrics and playoff predictors. The Vancouver Canucks came the closest to qualify as Stanley Cup Finals under-achievers after they lost against the Boston Bruins in 2011. Overall, the results tend to support the idea that the NHL fields extremely competitive playoff teams, that chance or other intangible factors play a significant role in NHL playoff outcomes and that playoff upsets will continue to occur regularly.

Police clearance rates and other forms of aggregated criminal justice data can be susceptible to statistical artefacts such as Simpson’s paradox (Yule-Simpson effect). Simpson’s paradox occurs when a trend apparent in separate data groups reverses itself once the groups are combined. Within 15 years of clearance rate data from the 50 largest Canadian police jurisdictions, 210 instances of Simpson’s paradox were discovered (annual mean = 14.0). These instances included four cases in which a reversal occurred simultaneously in all crime categories and subcategories. This finding suggests the need for caution when using clearance rates as a comparative measure of police performance, particularly between jurisdictions or time periods with different crime mixes. Criminal justice researchers, policy makers, and crime analysts should be aware of Simpson’s paradox and its potential effect on aggregated data. Finally, the possibility of a double reversal must be considered when attempting to resolve Simpson’s paradox.

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