Probabilistic weather forecasting with machine learning.

Weather forecasts are fundamentally uncertain, so predicting the range of probable weather scenarios is crucial for important decisions, from warning the public about hazardous weather to planning renewable energy use. Traditionally, weather forecasts have been based on numerical weather prediction (NWP), which relies on physics-based simulations of the atmosphere. Recent advances in machine learning (ML)-based weather prediction (MLWP) have produced ML-based models with less forecast error than single NWP simulations.

A new subjective well-being index using anchored best-worst scaling.

Subjective well-being (SWB) describes an individual's life evaluation. Direct elicitation methods for SWB via rating scales do not force individuals to trade-off among life domains, whilst best-worst scaling (BWS) approaches only provide relative measures. This paper instead offers a dual-response BWS task, where respondents nominate areas of most and least importance and satisfaction with respect to 11 SWB domains, whilst also eliciting anchoring points to obtain an absolute measure of domain satisfaction.

The impact of lamb and ewe mortality associated with dystocia on Australian and New Zealand sheep farms: A systematic review, meta-analysis and bio-economic model.

Dystocia contributes to lamb and ewe mortality in the periparturient period but impacts for extensive sheep production systems remain poorly understood. Here we show that lamb and ewe mortality associated with dystocia has important impacts on sheep production in Australia and New Zealand, and quantify financial impacts for the Australian sheep industry. A systematic review of the literature identified 11 publications published since 1990 that reported sheep mortality due to dystocia in Australia or New Zealand.