See the tutorial link for details.
Probablistic near-current interpolation
- Blending methods boosted model performances to ~ two-zero false negatives per species.
| Coast redwood SDM geo-classification (Sequoia sempervirens) | Standard deviations from multiple seeds/samples. |
|---|---|
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| Giant sequioa SDM geo-classification (Sequoiadendron giganteum) | Standard deviations from multiple seeds/samples. |
|---|---|
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| Joshua tree SDM geo-classification (Yucca brevifolia) | Standard deviations from multiple seeds/samples. |
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Python dependencies are listed in a requirements-py.txt file, including the library version numbers. You can replicate the environment your codebase needs by using virtualenv:
# This creates the virtual environment
cd $PROJECT-PATH
virtualenv ensemble-climate-projections
Then install the dependencies by referring to the requirements-py.txt:
# This installs the modules
pip install -r requirements-py.txt
# This activates the virtual environment
source ensemble-climate-projections/bin/activate