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BMP Bioenergy Reproducibility Package

Version 1.0.0 accompanying the manuscript "An Explainable Machine Learning Framework for Hypothesis Generation in Biochemical Methane Potential Prediction."

Archive and Release

Final Zenodo DOI: https://doi.org/10.5281/zenodo.21081788

GitHub release: https://github.com/Fashdan/BMP_Bioenergy_Reproducibility/releases/tag/v1.0.0

Quick Check

python run_all.py --quick-check --output-dir reproduction_quick_check

Full Regeneration

python run_all.py --output-dir reproduction_output

The workflow starts from the Lallement et al. source table, reconstructs the modeling table, applies the DM >= 15% and completeness filters, and regenerates the reported validation, prediction-error, target-sensitivity, explainability, uncertainty, and row-level transparency outputs.

Journal Supplementary Data Files

The journal-uploaded supplementary data files are:

  • Supplementary_Dataset_Row_Level_OOF_Predictions.csv
  • Supplementary_Dataset_Data_Dictionary.csv
  • Supplementary_Dataset_README.pdf

This repository contains the two CSV files above under supplementary_data_files/. It also contains supplementary_data_files/Supplementary_Dataset_README.txt, the repository-readable counterpart of the journal README PDF. The PDF is supplied separately to the journal and is not expected inside supplementary_data_files/; the TXT counterpart is not a fourth journal-uploaded supplementary data file.

Fold assignments, individual repeated out-of-fold predictions, selected hyperparameters, GridSearchCV candidate results, held-out SHAP outputs, scripts, environment files, and generated detailed outputs are repository-only and are stored under results/, scripts/, and the environment files in this repository. PUBLIC_REPOSITORY_FINAL_MANIFEST.csv and PUBLIC_REPOSITORY_SHA256SUMS.txt are non-self-referential integrity files: neither file lists itself or the other integrity file.

Source Data Citation

Lallement A, Peyrelasse C, Lagnet C, Barakat A, Schraauwers B, Maunas S, Monlau F. (2023). A Detailed Database of the Chemical Properties and Methane Potential of Biomasses Covering a Large Range of Common Agricultural Biogas Plant Feedstocks. Waste, 1(1), 195-227. https://doi.org/10.3390/waste1010014

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Code, data, results, and figures supporting an explainable machine learning framework for biochemical methane potential prediction.

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