Feedstock-Aware Machine Learning for Compost Maturity Classification: Cross-Domain Transfer Diagnosis and Threshold Calibration

Compost maturity screening supports safe land application and organic waste recycling, but germination index (GI) assays are not always available for rapid process assessment. This study performed a secondary GI-based maturity classification reconstruction using a published Nature Food composting dataset. From this source, 184 observations from 24 manure-based composting batch trajectories across five feedstock domains were retained when GI and routine physicochemical variables were available. GI values were converted into three maturity stages and a binary mature/non-mature endpoint, while the GI itself was excluded from model inputs. Logistic regression, random forest, and extra trees models were evaluated under random split, batch-aware group split, and leave-one-feedstock-domain-out validation. Random and group splits showed stronger apparent performance than cross-feedstock validation, indicating sensitivity to feedstock-domain transfer. In binary classification, the area under the receiver operating characteristic curve (ROC-AUC) remained relatively high in several model–domain combinations, whereas mature-class F1 declined, revealing a discrimination decision gap under default thresholds. Training-domain threshold calibration partially improved mature-class detection without using the held-out feedstock domain for threshold selection. These results support feedstock-aware validation and calibrated decision thresholds for sustainable compost maturity screening.