Objectives
- audit an ADQL query and propagate a cross-match decision
- Preserve a machine-readable provenance record.
- Quantify one uncertainty or sensitivity effect.
- Communicate a result with a figure, caption, and limitation.
Materials and setup
Use Python 3 and gaia_query_manifest.csv. Record file hash, row count, columns, source URL, release, access date, and provenance level.
Computed check: The 0.8 arcsec separation at 0.3 arcsec uncertainty gives weight 0.0285655.
Procedure
- Inspect the raw file and write its schema before filtering.
- Run the task and save query, code version, and output name.
- Perform the perturbation: vary match radius from 0.5 to 1.0 arcsec
- Compare baseline and perturbed results with units and direction of change.
- Write a caption distinguishing data, transformation, inference, and limitation.
Analysis record
| Record | Required entry |
|---|---|
| Provenance | Record endpoint, ADQL, columns, release, constraints, row count, timestamp, and citation. |
| Result | Formula or decision rule, value, units, and code reference. |
| Uncertainty | Statistical term plus calibration, selection, or model sensitivity. |
| Validation | Independent recomputation or schema check. |
Deliverables
Submit script or notebook, provenance manifest, labeled figure, 500-word methods memo, and revision note. Full credit requires traceable inputs, correct reasoning, and a specific limitation.
Data provenance
These supplied records are level-3 instructor-provided teaching artifacts, not new observations. Cite the official service and its citation policy.