averaging

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:".