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Stars-894 May 2026

| Metric | Target (3 months) | |--------|-------------------| | Adoption rate – % of articles with at least one accepted suggestion | ≥ 45 % | | Acceptance ratio – accepted / total suggestions shown | ≥ 60 % | | Average time saved – reduction in tagging time per article (measured via user session logs) | ≥ 30 seconds | | Error rate – failed suggestion API calls per 1,000 edits | ≤ 2 | | User satisfaction – NPS on “Tagging experience” survey question | ≥ +8 |


| Risk ID | Description | Likelihood | Impact | Mitigation | |---------|-------------|------------|--------|------------| | R1 | Radiation‑induced degradation of CZT detector. | Medium | High | Use radiation‑hard shielding (Al + Polyethylene); implement on‑board annealing cycles. | | R2 | Insufficient downlink bandwidth for burst data. | Low | High | Dual‑band Ka‑link for burst mode; on‑board data compression (lossless). | | R3 | Launch vehicle delay. | Medium | Medium | Backup launch provider (Ariane 6) contracted; schedule float. | | R4 | Software bugs in real‑time trigger algorithm. | Low | High | Independent verification & validation; hardware‑in‑the‑loop testing. | | R5 | Space‑weather induced anomalies (charging). | Medium | Medium | Incorporate conductive coatings; frequent charge‑control monitoring. | STARS-894

A Risk Register (Appendix C) tracks mitigation status quarterly. | Risk ID | Description | Likelihood |


| Level | Description | |-------|-------------| | Level‑0 | Raw telemetry, time‑ordered, de‑commutated. | | Level‑1 | Calibrated event list (energy correction, gain, dead‑time). | | Level‑2 | Science products – light curves, spectra, sky maps; transient alerts. | | Level‑3 | Derived catalogs (GRB, magnetar, TDE). | | Level | Description | |-------|-------------| | Level‑0

Processing pipelines are containerized (Docker) and executed on a cloud‑based HPC cluster (NASA CLOUD).

| Risk | Impact | Mitigation | |------|--------|------------| | NLP model returns irrelevant terms (low precision) | Poor author trust, extra manual work | Start with a conservative confidence threshold (e.g., 0.75); adjust based on analytics. | | Increased latency on slow connections | Frustrating editing experience | Debounce, cache recent suggestions, fallback to “unavailable” banner after 1 s timeout. | | Taxonomy drift (new terms not in taxonomy) | Suggestions cannot be mapped → no UI display | Show raw term in tooltip with “Add to taxonomy” link (future ticket). | | Over‑reliance on suggestions leading to tag homogenization | Loss of niche tags | Encourage authors to add custom tags; keep the UI for free‑form tag entry. | | Service outage of NLP micro‑service | Feature unavailable | Graceful fallback; continue publishing without suggestions. |


+----------------+          +-------------------+          +-------------------+
|   Content      |  POST    |   Tag Suggestion |  GET     |   Taxonomy Service|
|   Editor UI    |--------->|   Service (Node) |--------->|   (REST API)      |
+----------------+          +-------------------+          +-------------------+
        ^                         ^   ^   ^
        |                         |   |   |
        |  GET (suggestions)      |   |   |
        +-------------------------+   |   |
                                      |   |
                               +------+---+------+
                               | NLP Model (Python)|
                               +-------------------+
  • NLP Layer – Lightweight transformer (e.g., distilbert-base-uncased) fine‑tuned for keyword extraction. Hosted in a separate Python container, exposing a gRPC endpoint ExtractKeywords(text) → [term, score].
  • Analytics – Simple event collector (/api/analytics/tag-suggestion) writes to the existing Snowflake event table.