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Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q. and Liu, T.-Y. (2017) LightGBM: A Highly Efficient Gradient Boosting Decision Tree. In: Guyon, I., von Luxburg, U., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S. and Garnett, R., Eds., Advances in Neural Information Processing Systems 30, Curran Associates, Inc., 3146-3154.

has been cited by the following article:

• TITLE: When the Answer Is Constructed: A Target-Provenance Audit of Machine-Learning Benchmarking and Explainable AI for Logistics Loading-Bay Provision

  AUTHORS: Shakir Adeyemi Tewogbade, Linda Oraegbunam, Rebecca Omowaiye, Badmus Ayodeji

  KEYWORDS: Loading Bays, Freight Infrastructure Planning, Machine-Learning Benchmarking, Explainable AI, Target Validity, Target Leakage, Data Provenance, Spatial Validation

  JOURNAL NAME: Journal of Computer and Communications, Vol.14 No.11, November 11, 2026

  ABSTRACT: Predictive accuracy is informative for infrastructure planning only when the target represents the quantity decision-makers intend to assess. We audit this requirement using an open inventory of 47,682 logistics sites in England and Wales whose heavy-goods-vehicle loading-bay counts were modelled from floor area and facility type. A reconstructed deterministic rule matches 47,678 records (99.992%). We reproduce an initial five-model prediction pipeline and examine feature ablation, spatially blocked validation, attribution, and residual-based flags. The reproduced pipeline achieves R2 = 0.9871 and up to 98.7% of predictions within one bay. The decision tree using only floor area and category achieves R2 = 0.99985. Spatial-regional features alone perform poorly under blocked validation, and four engineered features directly or indirectly incorporate the target. In the deduplicated audit configuration, features outside the reconstructed rule receive up to 33.7% of mean absolute SHAP attribution. This measures departure from the rule, not necessarily an error by the explainer. Out of the 685 baseline flags, 684 match the reconstructed rule exactly, while cross-model flag agreement is low (mean Jaccard 0.25). For simulated one-bay reductions at eligible sites, Random Forest recall is 60.0% with the full feature set and 98.8% with rule inputs alone. These perturbations assess detection of constructed deviations, not actual operational inadequacy. We propose a Target Provenance and Validity Protocol that separates target validity, predictive performance, and decision validity before model outputs are used in planning.