LIRIL interpretation (tenet5.liril.infer)
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Top meta-amplified actors
Ranked by meta_amp = sqrt(axis_count) × sum(per_axis_amps) / 10. Multi-axis actors score higher due to sqrt weighting; high-per-axis actors can still out-rank them.
| # | Actor | Axes | Meta-amp | Total amp | Axis list |
|---|---|---|---|---|---|
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Actor × axis heat matrix
Each row is an actor, each column is one of 15 axes. Cell intensity = per-axis amplification. Empty cells = not Grover-marked in that axis.
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Method (reproducible)
1. Read
2. Canonicalize actor names (strip honorifics) to merge variants.
3. Build actor × axis amplitude matrix from
4. Compute
5. Rank actors by meta_amp. Publish top 20.
6. Consult LIRIL on
7. Hash the result, publish to NATS
Tool:
Data:
1. Read
{axis}_grover_decisionmakers.json for all 15 axes.2. Canonicalize actor names (strip honorifics) to merge variants.
3. Build actor × axis amplitude matrix from
marked_actors dict entries.4. Compute
meta_amp = sqrt(axis_count) * sum(per_axis_amps) / 10.0.5. Rank actors by meta_amp. Publish top 20.
6. Consult LIRIL on
tenet5.liril.infer for institutional interpretation.7. Hash the result, publish to NATS
tenet5.quantum.integrity.result.Tool:
tools/_quantum_meta_analysis.py
(SYSTEM_SEED 118400, reproduces deterministically from dossier JSONs).Data:
data/quantum_meta_portfolio.json (full) ·
data/quantum_meta_actors.json (compact).
Receipts:
full portfolio sha256: loading…
meta-grover merkle root: loading… · leaves: — · phase:
published → NATS
SYSTEM_SEED 118400 · generated at —
full portfolio sha256: loading…
meta-grover merkle root: loading… · leaves: — · phase:
—published → NATS
tenet5.quantum.integrity.resultSYSTEM_SEED 118400 · generated at —