Accurate production forecasts are essential for the integration of renewable energy sources into the power grid. This paper illustrates how to obtain probabilistic forecasts of wind power generation using gradient boosting trees and an ensemble of weather forecasts. To this end, we perform a comparative analysis across three state-of-the-art probabilistic prediction methods-conformalized quantile regression, natural gradient boosting and conditional diffusion models-all of which can be combined with tree-based machine learning. The methods are validated using four years of data for all Belgian offshore wind farms. We benchmark the models against the power curve and a calibrated wake model as well as a probabilistic method using stochastic variational Gaussian process regression. The tree-based models significantly reduce the mean absolute error in comparison to the deterministic baselines. Additionally, all three methods outperform the Gaussian process baseline in probabilistic skill, while two out of the three also improve point forecast accuracy. The conditional diffusion model attains the best performance, with improvements of 5% in mean absolute error and 12% in continuous rank probability score compared to the probabilistic baseline. Last, the results indicate an average improvement in point forecast accuracy of 17% by using an ensemble of weather forecasts instead of a single provider.
Livestock systems represent a considerable environmental challenge. In response, various scientists, non-governmental organisations, and policy makers claim that Western populations in particular need to sharply reduce meat consumption. Given people’s attachment to meat, many of these actors favour hard policy interventions based on a range of systemic financial and legal reforms that would go beyond mere nudging and the formulation of recommendations, including the top-down imposition of meat taxes and bans, as well as herd size reductions, which would lead to sharply higher prices. However, arguments in support of such policies tend to oversimplify the issue, ignoring regional variations, mitigation potential, and broader ecological and nutritional contexts. The focus of this article is on dietary greenhouse gas (GHG) emissions as a main target for environmental policymaking, with all livestock production in the West contributing 2.6
This study investigates the effects of non-word brand name (NWBN) length on brand name recognition and attitude. Using an experimental design, 98 participants were presented with 36 fictional brands (car, refrigerator, and smartphone), each paired with a non-word brand name varying in syllabic and phonemic length. The study collected 3,528 brand name attitude responses and, using a surprise two-alternative forced choice task, obtained an additional 3,528 recognition measurements. Contrary to previous research, results reveal that longer names are better recognized. Additionally, an inverted U-shaped relationship between NWBN length and attitude emerges when length is measured in phonemes rather than syllables. The originality of this study lies in its support for the layman belief that short names are generally better, while it shows that names shorter than three phonemes lead to less positive attitudes. This finding is important because it highlights the significance of phonemic length, an aspect previously overlooked, as studies have exclusively focused on syllabic length thus far.
To determine the impact of low versus atmospheric oxygen tension during only the pre-maturation phase of biphasic capacitation in vitro maturation. The study involved sibling oocytes (532 cumulus-oocyte complexes [COCs] from 20 participants [mean age 29.5 ± 2.5 years]) with polycystic ovary syndrome undergoing CAPA-IVM without gonadotrophins. After oocyte pick-up (OPU), COCs were randomized to undergo pre-maturation under low or atmospheric oxygen, then IVM culture at 20
Large language models have demonstrated remarkable progress in mathematical reasoning, leveraging chain-of-thought and reinforcement learning. However, many open questions remain regarding the interplay between reasoning token usage and accuracy gains. In particular, when comparing models across generations, it is unclear whether improved performance results from longer reasoning chains or more effective reasoning. We systematically analyze reasoning chain length across o1-mini and o3-mini variants on the Omni-MATH benchmark, finding that o3-mini (m) achieves superior accuracy without requiring longer reasoning chains than o1-mini. Moreover, we show that accuracy generally declines as reasoning chains grow across all models and compute settings, even when controlling for difficulty of the questions. This accuracy drop is significantly smaller in more proficient models, suggesting that new generations of reasoning models use test-time compute more effectively. Finally, we highlight that while o3-mini (h) achieves a marginal accuracy gain over o3-mini (m), it does so by allocating substantially more reasoning tokens across all problems, even the ones that o3-mini (m) can already solve. These findings provide new insights into the relationship between model capability and reasoning length, with implications for efficiency, scaling, and evaluation methodologies.
