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Predicting maximum pitting corrosion depth in buried transmission pipelines: Insights from tree-based machine learning and identification of influential factors
Research article (Process Safety and Environmental Protection, 2024) · cited 40× · AI/ML
Predicting maximum pitting corrosion depth in buried transmission pipelines: Insights from tree-based machine learning and identification of influential factors
Summary
Predicting maximum pitting corrosion depth in buried transmission pipelines: Insights from tree-based machine learning and identification of influential factors is a scholarly article[1].
Key Facts
Predicting maximum pitting corrosion depth in buried transmission pipelines: Insights from tree-based machine learning and identification of influential factors's instance of is recorded as scholarly article[2].
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Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). Predicting maximum pitting corrosion depth in buried transmission pipelines: Insights from tree-based machine learning and identification of influential factors. Retrieved May 24, 2026, from https://4ort.xyz/entity/predicting-maximum-pitting-corrosion-depth-in-buried-transmission-pipelines-insights-from-tree-based-machine-learning-an
MLA“Predicting maximum pitting corrosion depth in buried transmission pipelines: Insights from tree-based machine learning and identification of influential factors.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/predicting-maximum-pitting-corrosion-depth-in-buried-transmission-pipelines-insights-from-tree-based-machine-learning-an.
BibTeX@misc{4ortxyz_predicting-maximum-pitting-corrosion-depth-in-buried-transmission-pipelines-insights-from-tree-based-machine-learning-an_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Predicting maximum pitting corrosion depth in buried transmission pipelines: Insights from tree-based machine learning and identification of influential factors}}, year = {2026}, url = {https://4ort.xyz/entity/predicting-maximum-pitting-corrosion-depth-in-buried-transmission-pipelines-insights-from-tree-based-machine-learning-an}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Predicting maximum pitting corrosion depth in buried transmission pipelines: Insights from tree-based machine learning and identification of influential factors — https://4ort.xyz/entity/predicting-maximum-pitting-corrosion-depth-in-buried-transmission-pipelines-insights-from-tree-based-machine-learning-an (retrieved 2026-05-24)
