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rosielickorish
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- config.yaml +1 -1
README.md
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# granite-geospatial-ocean
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The granite-geospatial-ocean foundation model was jointly developed by IBM and STFC as part of a collaboration with the University of Exeter and Plymouth Marine Lab under the UK HNCDI programme. This pre-trained model supports a range of potential use cases in ocean ecosystem health, fisheries management, pollution and other ocean processes that can be monitored using ocean colour observations. We provide an example to fine tune the model to quantify primary production by phytoplankton (carbon sequestration which determine's the ocean's role in climate change).
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## Architecture Overview
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### Citation
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If this model helped your research, please cite [Granite-ocean-gfm](https://
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<!-- ```
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@article{Granite-geospatial-ocean-Preprint,
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# granite-geospatial-ocean
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The granite-geospatial-ocean foundation model was jointly developed by IBM and STFC as part of a collaboration with the University of Exeter and Plymouth Marine Lab under the UK HNCDI programme. This pre-trained model supports a range of potential use cases in ocean ecosystem health, fisheries management, pollution and other ocean processes that can be monitored using ocean colour observations. We provide an example to fine tune the model to quantify primary production by phytoplankton (carbon sequestration which determine's the ocean's role in climate change). For full details of the model pre-training, fine-tuning and evaluation, please see the [preprint paper](https://arxiv.org/abs/2509.21273).
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## Architecture Overview
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### Citation
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If this model helped your research, please cite [Granite-ocean-gfm](https://arxiv.org/abs/2509.21273) in your publications.
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<!-- ```
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@article{Granite-geospatial-ocean-Preprint,
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config.yaml
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2.67577808
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],
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"origin_url": "https://huggingface.co/ibm-granite/granite-geospatial-ocean",
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"paper_ids": "https://
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}
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}
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2.67577808
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],
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"origin_url": "https://huggingface.co/ibm-granite/granite-geospatial-ocean",
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"paper_ids": "https://arxiv.org/abs/2509.21273"
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}
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}
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