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--- |
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license: apache-2.0 |
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tags: |
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- coastal-dynamics |
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- oceanography |
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- wave-prediction |
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- physics-informed |
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- neural-operator |
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- climate |
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- earth-science |
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- pytorch |
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language: |
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- en |
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pipeline_tag: other |
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library_name: pytorch |
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--- |
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# Naturecode Coastal Dynamics |
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**State-of-the-Art AI for Coastal Wave Dynamics and Ocean Modeling** |
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*Initial Release.0 - Foundation Release* |
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*Designed to augment/replace traditional numerical models like MIKE 21* |
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--- |
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## Overview |
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**Naturecode Coastal Dynamics** is a cutting-edge deep learning system for predicting coastal wave dynamics, sediment transport, and ocean conditions. This foundation release establishes the core architecture incorporating the latest advances from 2025-2026 oceanographic AI research. |
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--- |
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## Architecture Features |
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| Feature | Source | Description | |
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|---------|--------|-------------| |
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| Fourier Neural Operator (FNO) | Li et al. 2021 | Spectral convolutions for PDE solving | |
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| Mixture-of-Time (MoT) | FuXi-Ocean NeurIPS 2025 | Adaptive temporal fusion for multi-scale forecasting | |
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| Adaptive Layer Normalization (AdaLN) | FuXi-Ocean NeurIPS 2025 | Context-aware normalization | |
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| Earthformer Cuboid Attention | NeurIPS 2022 + Science Advances 2024 | Remote swell detection from distant storms | |
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| Mamba Neural Operator | J. Comp Physics Dec 2025 | 90% error reduction over Transformers | |
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| MC Dropout | XWaveNet | Uncertainty quantification via Monte Carlo dropout | |
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| Energy Conservation Loss | OceanCastNet | Physical constraint for long-term stability | |
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| Diffusion Refinement | OmniCast/GenCast | Probabilistic ensemble forecasting | |
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| VAE Latent Compression | OmniCast NeurIPS 2025 | Efficient latent-space diffusion | |
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| Extreme Event Detection | XWaveNet | Multi-threshold wave height exceedance prediction | |
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### Model Statistics |
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| Specification | Value | |
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|---------------|-------| |
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| Parameters | 14,027,854 (14M) | |
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| Architecture | Hybrid FNO + Mamba + Swin Transformer | |
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| Input Channels | 8 | |
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| Output Channels | 5 | |
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| Training Epochs | 500 | |
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| Training Hardware | 8x NVIDIA H100 GPUs | |
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| Training Data | 18.4M real ocean observations | |
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--- |
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## Input/Output Specification |
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### Input Channels (8) |
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| Channel | Description | Units | |
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|---------|-------------|-------| |
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| 0 | Bathymetry | meters (negative = depth) | |
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| 1 | Wind U-component | m/s | |
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| 2 | Wind V-component | m/s | |
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| 3 | Previous wave height | meters | |
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| 4 | Previous U-velocity | m/s | |
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| 5 | Previous V-velocity | m/s | |
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| 6 | Previous surface elevation | meters | |
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| 7 | Time encoding | normalized [0, 1] | |
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### Output Channels (5) |
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| Channel | Description | Units | |
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|---------|-------------|-------| |
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| 0 | Significant wave height | meters | |
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| 1 | U-velocity | m/s | |
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| 2 | V-velocity | m/s | |
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| 3 | Surface elevation (eta) | meters | |
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| 4 | Sediment transport | kg/m2/s | |
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--- |
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## Intended Use |
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### Primary Use Cases |
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- Coastal Engineering: Wave prediction for harbor design, breakwater planning |
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- Climate Adaptation: Storm surge and extreme event forecasting |
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- Environmental Monitoring: Sediment transport and coastal erosion prediction |
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- Marine Operations: Sea state forecasting for shipping and offshore operations |
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- Research: Accelerating ocean/coastal simulations (1000x faster than MIKE 21) |
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### Out-of-Scope Uses |
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- Real-time tsunami warning (requires specialized systems) |
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- Operational weather forecasting without domain validation |
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- Areas without adequate bathymetric data |
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--- |
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## Training Data |
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### Data Sources |
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1. Synthetic Physics-Based Data: Generated using simplified shallow water equations |
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2. NOAA NDBC Buoy Data: Real ocean observations from 60 buoys (2015-2025) |
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- Records: 18.4 million timestamped observations |
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- Coverage: Pacific, Atlantic, Gulf of Mexico, Hawaii |
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- Variables: Wave height, period, direction, wind, SST, pressure |
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--- |
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## How to Use |
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### Installation |
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```bash |
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pip install torch numpy |
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``` |
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### Basic Inference |
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```python |
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import torch |
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from model import CoastalDynamicsModel |
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# Load model |
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model = CoastalDynamicsModel(embed_dim=128, dropout=0.1) |
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checkpoint = torch.load('pytorch_model.pt', map_location='cpu') |
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model.load_state_dict(checkpoint['model_state_dict']) |
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model.eval() |
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# Prepare input (B, 8, H, W) |
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inputs = torch.randn(1, 8, 128, 128) |
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# Forward pass |
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with torch.no_grad(): |
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outputs = model(inputs, return_uncertainty=True, return_extreme_probs=True) |
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# Access outputs |
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wave_height = outputs['mean'][:, 0] # Significant wave height |
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uncertainty = outputs['std'][:, 0] # Prediction uncertainty |
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extreme_probs = outputs['extreme_probs'] # P(wave > 2m, 4m, 6m, 8m) |
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``` |
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### Uncertainty Quantification |
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```python |
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# Monte Carlo Dropout + Diffusion ensemble |
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results = model.predict_with_uncertainty( |
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inputs, |
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num_mc_samples=20, # Epistemic uncertainty |
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num_diffusion_samples=10 # Aleatoric uncertainty |
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) |
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print(f"Mean prediction: {results['mean'].shape}") |
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print(f"MC uncertainty: {results['mc_std'].shape}") |
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print(f"Diffusion uncertainty: {results['diffusion_std'].shape}") |
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print(f"Extreme event probs: {results['extreme_probs'].shape}") |
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``` |
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--- |
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## Performance |
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| Metric | Value | Description | |
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|--------|-------|-------------| |
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| Charbonnier Loss | 0.035 | Robust L1-like loss | |
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| Physics Loss | 0.067 | Physical consistency | |
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| NLL (Diffusion) | -3.5 | Log-likelihood | |
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| Energy Conservation | 0.000 | Perfect conservation | |
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| Best Total Loss | -1.01 | Combined metric | |
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--- |
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## Limitations |
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1. Spatial Resolution: Optimized for 128x128 grids |
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2. Temporal Resolution: Best for 6-hourly predictions |
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3. Geographic Bias: Training data primarily from US coastal waters |
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4. Extreme Events: Rare events (>99th percentile) have inherent prediction challenges |
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5. Bathymetry Dependency: Requires accurate bathymetric input |
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--- |
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## Environmental Impact |
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| Metric | Value | |
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|--------|-------| |
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| Training Hardware | 8x NVIDIA H100 GPUs | |
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| Training Time | ~4 hours | |
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| Estimated CO2 | ~15 kg CO2eq | |
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| Cloud Provider | Google Cloud (renewable mix) | |
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--- |
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## Citation |
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```bibtex |
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@misc{naturecode_coastal_dynamics_2026, |
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title={Naturecode Coastal Dynamics: Physics-Informed Deep Learning for Ocean Wave Prediction}, |
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author={Naturecode Team}, |
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year={2026}, |
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version={1.0}, |
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publisher={Hugging Face}, |
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url={https://huggingface.co/Naturecode/coastal-dynamics} |
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} |
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``` |
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--- |
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## License |
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This model is released under the Apache 2.0 License. |
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--- |
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## Acknowledgments |
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This model builds upon research from: |
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- FuXi-Ocean (NeurIPS 2025) |
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- OmniCast (NeurIPS 2025) |
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- OceanCastNet |
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- XWaveNet |
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- Earthformer (NeurIPS 2022) |
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- Mamba Neural Operator (J. Comp Physics 2025) |
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- NOAA National Data Buoy Center |
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--- |
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## Contact |
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For questions, collaborations, or access requests: |
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- Organization: Naturecode |
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--- |
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Built by Naturecode - Advancing coastal science through AI |
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