1 citations · 4 across the 27 of their papers we have counts for
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GREAT: Generalizable Representation Enhancement via Auxiliary Transformations for Zero-Shot Environmental Prediction
Shiyuan Luo, Chonghao Qiu, Runlong Yu +2
Environmental modeling faces critical challenges in predicting ecosystem dynamics across unmonitored regions due to limited and geographically imbalanced observation data. This cha…
Geo-Aware Models for Stream Temperature Prediction across Different Spatial Regions and Scales
Shiyuan Luo, Runlong Yu, Shengyu Chen +4
Understanding environmental ecosystems is vital for the sustainable management of our planet. However,existing physics-based and data-driven models often fail to generalize to vary…
Truth Without Comprehension: A BlueSky Agenda for Steering the Fourth Mathematical Crisis
Runlong Yu, Xiaowei Jia
Machine-generated proofs are poised to reach large-scale, human-unreadable artifacts. They foreshadow what we call the Fourth Mathematical Crisis. This crisis crystallizes around t…
Learning to Retrieve for Environmental Knowledge Discovery: An Augmentation-Adaptive Self-Supervised Learning Framework
Shiyuan Luo, Runlong Yu, Chonghao Qiu +5
The discovery of environmental knowledge depends on labeled task-specific data, but is often constrained by the high cost of data collection. Existing machine learning approaches u…
RAG for Geoscience: What We Expect, Gaps and Opportunities
Runlong Yu, Shiyuan Luo, Rahul Ghosh +3
Retrieval-Augmented Generation (RAG) enhances language models by combining retrieval with generation. However, its current workflow remains largely text-centric, limiting its appli…
From Pixels to Places: A Systematic Benchmark for Evaluating Image Geolocalization Ability in Large Language Models
Lingyao Li, Runlong Yu, Qikai Hu +4
Image geolocalization, the task of identifying the geographic location depicted in an image, is important for applications in crisis response, digital forensics, and location-based…
LLM-based Evaluation Policy Extraction for Ecological Modeling
Qi Cheng, Licheng Liu, Qing Zhu +4
Evaluating ecological time series is critical for benchmarking model performance in many important applications, including predicting greenhouse gas fluxes, capturing carbon-nitrog…
Knowledge Guided Encoder-Decoder Framework: Integrating Multiple Physical Models for Agricultural Ecosystem Modeling
Qi Cheng, Licheng Liu, Yao Zhang +5
Agricultural monitoring is critical for ensuring food security, maintaining sustainable farming practices, informing policies on mitigating food shortage, and managing greenhouse g…
Multi-Scale Graph Learning for Anti-Sparse Downscaling
Yingda Fan, Runlong Yu, Janet R. Barclay +4
Water temperature can vary substantially even across short distances within the same sub-watershed. Accurate prediction of stream water temperature at fine spatial resolutions (i.e…
X-MethaneWet: A Cross-scale Global Wetland Methane Emission Benchmark Dataset for Advancing Science Discovery with AI
Yiming Sun, Shuo Chen, Shengyu Chen +9
Methane (CH) is the second most powerful greenhouse gas after carbon dioxide and plays a crucial role in climate change due to its high global warming potential. Accurately mod…
Foundation Models for Environmental Science: A Survey of Emerging Frontiers
Runlong Yu, Shengyu Chen, Yiqun Xie +3
Modeling environmental ecosystems is essential for effective resource management, sustainable development, and understanding complex ecological processes. However, traditional data…
A Survey of Foundation Models for Environmental Science
Runlong Yu, Shengyu Chen, Yiqun Xie +1
Modeling environmental ecosystems is essential for effective resource management, sustainable development, and understanding complex ecological processes. However, traditional meth…
Physics-Guided Foundation Model for Scientific Discovery: An Application to Aquatic Science
Runlong Yu, Chonghao Qiu, Robert Ladwig +3
Physics-guided machine learning (PGML) has become a prevalent approach in studying scientific systems due to its ability to integrate scientific theories for enhancing machine lear…