feat(notebooks): add 03_carbon_analysis carbon stock estimation notebook#47
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End-to-end notebook that loads (or simulates) a biomass-labelled spectral dataset, trains a Random Forest BiomassRegressor, evaluates RMSE/MAE/R^2/MAPE, converts biomass predictions to carbon and CO2e using IPCC defaults, plots feature importances, and persists the model + metrics for the analytics API and the model-card pipeline. Falls back to a synthetic dataset when the labelled parquet file is not present, so the notebook is runnable in CI.
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Goldokpa
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May 5, 2026
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Goldokpa
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Nice end-to-end demonstration — the synthetic-fallback dataset means this stays runnable in CI without GEE access. Feature importances come out where you'd expect (NDVI dominant) and the saved metrics.json plugs straight into the model-card generator.
LGTM.
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Summary
notebooks/03_carbon_analysis.ipynb— end-to-end walk-through of training aBiomassRegressor, evaluating it, converting biomass to carbon and CO2e, plotting feature importances, and persisting the model + metrics.data/biomass/biomass_samples.parquetif present; otherwise simulates a 5,000-sample synthetic dataset so the notebook is runnable in CI.models_pretrained/biomass_rf.pkland the metrics tooutputs/carbon/metrics.jsonfor the analytics API and model-card pipeline.Why
Sprint deliverable: "Create 03_carbon_analysis.ipynb — Carbon estimation analysis."
Test plan
python -c 'import json; json.load(open("notebooks/03_carbon_analysis.ipynb"))'— valid notebook JSON.papermill notebooks/03_carbon_analysis.ipynb /tmp/out.ipynb.Notes for reviewers
BiomassRegressorintroduced in feat(models): add biomass and carbon-stock regression module #46; merge that first.OUTPUTSresolves correctly whether the notebook is run from the repo root or fromnotebooks/.