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Attribute an HPC I/O regression from aligned metric evidence.
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Basé sur la classification professionnelle SOC
Compare scientific variables with units, ranges, and quality caveats.
Inspect dataset structure before analysis or visualization.
Evaluate data quality from observed structure and summary evidence.
Choose visual checks that match the inspected data.
Route dataset questions to structure, analysis, or visualization experts.
Choose a plot that confirms numeric conversion integrity.
| name | attribute_io_regression |
| title | Attribute I/O Regression |
| description | Attribute an HPC I/O regression from aligned metric evidence. |
Build the root-cause hypothesis from aligned evidence. Prefer explanations that account for the largest changed timings while remaining consistent with stable workload metrics.
State the confidence and caveats. If the evidence only supports a likely cause, say so. Mention follow-up measurements that would confirm the hypothesis.
