| name | averaging |
| description | Use this skill when calculating weighted or unweighted averages for ENSDF nuclear data using Java_Average.py. Enforces exact transcription of the Suggested Adopted Result, minimum-uncertainty rule, and lifetime uncertainty limit 99. Suitable for adopting measured values from multiple publications.
|
| argument-hint | ["VALUE1 UNC1 VALUE2 UNC2 ..."] |
ENSDF Averaging
ENSDF 80-column data record and field definitions, structural rules, column positions, and uncertainty notation: .github/agents/ENSDF-Agent.agent.md. Spot-check policy: .github/copilot-instructions.md.
When
Run Java_Average.py any time you need to adopt a value from 2+ measurements across different papers.
How
Numeric mode — comma after each pair for readability (optional):
python .github/scripts/Java_Average.py 19.7 1.3, 22 4, 21.5 1.5
Comment mode — feed the existing cL T$ comment directly:
python .github/scripts/Java_Average.py --comment "19.7 ps {I13} (1970Br10) and 22 ps {I4} (1975Sm02)"
What to adopt
When user requests code Java_Average.py for calculating averages, follow these rules with absolute precision and zero tolerance for deviation:
- Always use exact Java code "Suggested Adopted Result" value without recalculation or substitution
- Use exact uncertainty value provided by Java code (automatically applies rule: adopted uncertainty ≥ any individual input uncertainty)
- Check whether Java suggests weighted or unweighted average in output comments
- Use whichever method Java code explicitly recommends
- Transcribe all values character-for-character without rounding, adjustment, or omitting units
- Never recalculate averages by yourself
- Never use unrecommended uncertainty results
- Never substitute weighted/unweighted averages contrary to Java's recommendation
Minimum Uncertainty Rule
ENSDF-Specific Requirement: Adopted uncertainty ≥ any individual input uncertainty.
Rationale: Prevents averaging from artificially reducing systematic uncertainty below best single measurement. Maintains conservative uncertainty estimates in nuclear data evaluation. Java_Average.py automatically enforces this.
- Statistical avg < min input uncertainty → Adopted = min input uncertainty
- Statistical avg ≥ min input uncertainty → Adopted = statistical average
Example: Averaging 665.56±0.05 and 665.6±0.1 yields statistical uncertainty 0.0447, but adopted uncertainty becomes 0.05 (matches smallest input).
Gotchas
[critical=X] is display-only. The tool decides Weighted vs. Unweighted using a hardcoded threshold of 3.5, not the displayed chi² critical value.- Lifetimes use full precision (uncertainty limit 99): write
197 fs {I50}, not 2.0E2 {I5}.
- One value per paper. Comment mode skips any value before "average of" (it's the previous result) and stops at "Other:".
