what is sortkey in the following Article
"DIANA Sort Center Model Design 
I. Background

DIANA (Data, Insight, Action, aNd Accountability) is a model to support users on diagnosing warehouse throughput issues and recommending remedial actions. In 2022, AR SWRDS team researched and developed a ‘DIANA-SC’ model for sortation floors across AR network to identify the root causes of sort center performance degradation and quantitatively attributes the root causes through Causal-Inference mathematical methods. Based on the data-driven diagnosis, the model generates actionable insights and directs mitigation recommendations to help users improve AR warehouse productivity.

In Dec 2022, GSS, SWRDS and Customer Success Team collaborated and integrated the science model of DIANA-SC into Upper Mezz Quarter Back Command Center (QBCC) to pilot and test the use cases for Sortation Vertical and Operations. This pilot tool provides real-time diagnosis and guidance for operation teams to tackle down floor health constraint including amnesty and disablements. 
II. Summary

DIANA-SC diagnoses the root causes of throughput degradation within the sort flow process which starts from induct stations on Mezz Floor to the end of chutes on Ground Floor. Within this section, minimizing the Recirculation Rate and Drive Gap are primary goals to optimize the throughput. DIANA-SC proactively detects high severity andons that contribute to floor recirculation and drive gap and provide data-driven remediation guidance. The model output of root cause attributions can be used by 1) Operation Teams to get immediate guidance and resolve high severity andons at early stage; 2) Technical Support Teams and other business stakeholders to retrospect long-term floor performance and dive deep in degradation issues based on historical aggregated data.
III. Tenets
•	Actionable: DIANA-SC is designed to identify and highlight andons that operation team can take immediate actions to resolve independently. Mitigating high severity risks at early stage is always preferred rather than waiting till it grows to a floor level disaster. 
•	Explainable: DIANA-SC detects and quantifies the severity of each NAME_1 through data-driven process based on its contribution to floor recirculation volume. It captures ‘reject to recirc’ events of packages to evaluate the severity of andons through causal inference model, which is fully explainable to operation teams.
•	Comprehensive: DIANA-SC is designed to provide comprehensive analysis to scan all the NAME_1 types that may contribu Provide two versions of response separated with exactly 6 asterisk symbols: ******.
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