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Brendon Woodford

Publications and source records attributed to Brendon Woodford.

2 recordsLinked to original sources

Blackening Cryosphere: Revealing Hotspot Shifts and HGB-Based Forecasting of Absorbing Aerosol Threats over the Himalayan Frozen Frontiers

Black carbon and mineral dust are key absorbing aerosols that influence atmospheric radiation and increasingly threaten global cryospheric stability. This study examines the long-range transport and seasonal variability of these aerosols over Pakistan and their movement toward the western Himalayas. Using satellite-derived Absorption Aerosol Optical Depth (AAOD) data from 2019 to mid-2025, we analyse their spatiotemporal behaviour across Pakistan's urban lowlands and high-altitude regions. Fifteen-day aggregated AAOD fields are used to track seasonal transport into glaciated terrain, where deposited aerosols can darken snow and ice and accelerate melt. For high-AAOD events, a probabilistic forecasting approach based on machine learning (ML) was developed. Using geographical, seasonal, and lagged indicators, a histogram-based gradient boosting classifier was trained to predict AAOD exceedance one step in advance. ROC-AUC, PR-AUC, and the Brier score were used to assess the model's performance. The results show high predictive capacity and good probability calibration, with values of 0.791, 0.269, and 0.028, respectively. Forecasts indicate that areas adjacent to Himalayan glaciers consistently exhibit the highest probability of increasing AAOD, signalling an elevated risk of aerosol-induced snowmelt.

physics.ao-ph↗

GBM Returns the Best Prediction Performance among Regression Approaches: A Case Study of Stack Overflow Code Quality

Practitioners are increasingly dependent on publicly available resources for supporting their knowledge needs during software development. This has thus caused a spotlight to be paced on these resources, where researchers have reported mixed outcomes around the quality of these resources. Stack Overflow, in particular, has been studied extensively, with evidence showing that code resources on this platform can be of poor quality at times. Limited research has explored the variables or factors that predict code quality on Stack Overflow, but instead has focused on ranking content, identifying defects and predicting future content. In many instances approaches used for prediction are not evaluated to identify the best techniques. Contextualizing the Stack Overflow code quality problem as regression-based, we examined the variables that predict Stack Overflow (Java) code quality, and the regression approach that provides the best predictive power. Six approaches were considered in our evaluation, where Gradient Boosting Machine (GBM) stood out. In addition, longer Stack Overflow code tended to have more code violations, questions that were scored higher also attracted more views and the more answers that are added to questions on Stack Overflow the more errors were typically observed in the code that was provided. Outcomes here point to the value of the GBM ensemble learning mechanism, and the need for the practitioner community to be prudent when contributing and reusing Stack Overflow Java coding resource.

cs.SE↗