Science Archaeology 3D map of Easter Island takes you places visitors aren't allowed One of the world's most isolated islands is open to virtual tourists. Breakthroughs, discoveries, and DIY tips sent every weekday. Nestled in the South Pacific Ocean, some 6,000 people live on the most isolated, inhabited island in the world: Rapa Nui. Known to many as Easter Island, a name Dutch explorer Jacob Roggeveen coined after landing on the island on Easter Sunday 1722, Rapa Nui is roughly double the size of Disney World, or 63.2 square miles. And every year, some 100,000 people visit the remote island to see the famed 13-foot-tall moai statues or Easter Island heads .

Community science observational datasets are useful in epidemiology and ecology for modeling species distributions, but the heterogeneous nature of the data presents significant challenges for standardization, data quality assurance and control, and workflow management. In this paper, we present a data workflow for cleaning and harmonizing multiple community science datasets, which we implement in a case study using eBird, iNaturalist, GBIF, and other datasets to model the impact of highly pathogenic avian influenza in populations of birds in the subantarctic. We predict population sizes for several species where the demographics are not known, and we present novel estimates for potential mortality rates from HPAI for those species, based on a novel aggregated dataset of mortality rates in the subantarctic.

Perch 2.0 is a supervised bioacoustics foundation model pretrained on 14,597 species, including birds, mammals, amphibians, and insects, and has state-of-the-art performance on multiple benchmarks. Given that Perch 2.0 includes almost no marine mammal audio or classes in the training data, we evaluate Perch 2.0 performance on marine mammal and underwater audio tasks through few-shot transfer learning. We perform linear probing with the embeddings generated from this foundation model and compare performance to other pretrained bioacoustics models. In particular, we compare Perch 2.0 with previous multispecies whale, Perch 1.0, SurfPerch, AVES-bio, BirdAVES, and Birdnet V2.3 models, which have open-source tools for transfer-learning and agile modeling. We show that the embeddings from the Perch 2.0 model have consistently high performance for few-shot transfer learning, generally outperforming alternative embedding models on the majority of tasks, and thus is recommended when developing new linear classifiers for marine mammal classification with few labeled examples.

Understanding the structure of the homology embedding can thus disclose geometric or topological information from the data.

Climate change is amplifying extreme weather and climate events worldwide [1]. Anthropogenic greenhouse gas emissions have disrupted the Earth's climate system, driving more frequent and severe heatwaves [2], cold spells [3], heavy precipitation [4], agricultural droughts [5], and tropical cyclones (TCs) [6]. Between 2016 and 2024, daily land temperature records show that extreme heat events occurred over four times more often than expected, while cold records declined by half [7]. These unprecedented shifts threaten human health [8, 9], infrastructure [10, 11], food security [12], biodiversity [13], and global economies [14, 15]. Therefore, reliable climate projections are essential for effective mitigation and adaptation strategies [16-18]. The Coupled Model Intercomparison Project (CMIP) [19] provides a foundation for global climate projections. Since its launch in 1995, CMIP has coordinated systematic evaluation of coupled general circulation models (GCMs). CMIP5 introduced Representative Concentration Pathways (RCPs), while CMIP6 extended this framework by incorporating Shared Socioeconomic Pathways (SSPs) through ScenarioMIP, enabling consistent simulations of emissions and socioeconomic trajectories to 2100 and facilitating integrated assessment of climate risks [20]. These advances have greatly enhanced the scientific and policy relevance of climate projections.

The tiny shark and a ghost-like crab are two of the latest species uncovered in a yearslong expedition. Breakthroughs, discoveries, and DIY tips sent every weekday. Oceanographers scouring the waters off of Western Australia have discovered two new deep-sea oddities . On October 6, Australia's Commonwealth Scientific and Industrial Research Organization (CSIRO) showcased these new species originally collected in 2022: a bioluminescent lanternshark and a tiny, semi-translucent porcelain crab . The team revealed two of its initial finds--the painted hornshark and the ridged-egg catshark --in 2023.

Data augmentation with generative adversarial networks (GANs) has been popular for class imbalance problems, mainly for pattern classification and computer vision-related applications. Extreme value forecasting is a challenging field that has various applications from finance to climate change problems. In this study, we present a data augmentation framework for extreme value forecasting. In this framework, our focus is on forecasting extreme values using deep learning models in combination with data augmentation models such as GANs and synthetic minority oversampling technique (SMOTE). We use deep learning models such as convolutional long short-term memory (Conv-LSTM) and bidirectional long short-term memory (BD-LSTM) networks for multistep ahead prediction featuring extremes. We investigate which data augmentation models are the most suitable, taking into account the prediction accuracy overall and at extreme regions, along with computational efficiency. We also present novel strategies for incorporating data augmentation, considering extreme values based on a relevance function. Our results indicate that the SMOTE-based strategy consistently demonstrated superior adaptability, leading to improved performance across both short- and long-horizon forecasts. Conv-LSTM and BD-LSTM exhibit complementary strengths: the former excels in periodic, stable datasets, while the latter performs better in chaotic or non-stationary sequences.

As a developing country, Fiji is facing rapid urbanisation, which is visible in the massive development projects that include housing, roads, and civil works. In this study, we present machine learning and remote sensing frameworks to compare land use and land cover change from 2013 to 2024 in Nadi, Fiji. The ultimate goal of this study is to provide technical support in land cover/land use modelling and change detection. We used Landsat-8 satellite image for the study region and created our training dataset with labels for supervised machine learning. We used Google Earth Engine and unsupervised machine learning via k-means clustering to generate the land cover map. We used convolutional neural networks to classify the selected regions' land cover types. We present a visualisation of change detection, highlighting urban area changes over time to monitor changes in the map.

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Despite growing research on bias in large language models (LLMs), most work has focused on gender and race, with little attention to religious identity. This paper explores how religion is internally represented in LLMs and how it intersects with concepts of violence and geography. Using mechanistic interpretability and Sparse Autoencoders (SAEs) via the Neuronpedia API, we analyze latent feature activations across five models. We measure overlap between religion- and violence-related prompts and probe semantic patterns in activation contexts. While all five religions show comparable internal cohesion, Islam is more frequently linked to features associated with violent language. In contrast, geographic associations largely reflect real-world religious demographics, revealing how models embed both factual distributions and cultural stereotypes. These findings highlight the value of structural analysis in auditing not just outputs but also internal representations that shape model behavior.