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		<title>Hidden data leakage inflates AI model accuracy in cement plant records</title>
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		<pubDate>Sat, 24 Oct 2026 11:53:34 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[cement clinker production]]></category>
		<category><![CDATA[industrial data]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[Multivariate Regression]]></category>
		<category><![CDATA[ontology]]></category>
		<category><![CDATA[PLC-SCADA]]></category>
		<category><![CDATA[process industries]]></category>
		<category><![CDATA[setpoint detection]]></category>
		<category><![CDATA[staircase score]]></category>
		<category><![CDATA[target leakage]]></category>
		<category><![CDATA[variable]]></category>
		<category><![CDATA[variable ontology]]></category>
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					<description><![CDATA[A two-evidence auditing method combining name-based and behavioral screening exposed severe target leakage in a cement plant's PLC-SCADA data, cutting a model's R² from 0.9999 to 0.8109.]]></description>
		
		
		
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