Kartu Sistem
System Card v2.0Version 2.0 · · Governance 2.0 public evidence surface
Governance 2.0 Overview
This page is part of the starnum public Governance 2.0 surface and uses the same evidence layer as the system card, data governance, transparency report, use policy, and security policy.
Governance Summary
This page is the canonical public system disclosure for platform behavior, model/provider boundaries, and evidence-backed governance.
Scope
System behavior, AI assistance boundaries, public claims, governance metrics, provider alignment, and release integrity.
Implementation Status
Version 2.0 is the source structure used to align the other public governance pages.
Versi 1.0 · · Mengacu pada kerangka Anthropic System Card
Kartu Sistem ini memperluas Kartu Model dan menjelaskan kemampuan sistem menyeluruh starnum.com.tw, infrastruktur basis pengetahuan, pipeline berbantuan AI, mekanisme jaminan mutu, dan batasan yang diketahui.
1. Ringkasan Sistem
| Nama Platform | Starnum Platform |
| Versi | v5.0 (2026-04-12) |
| Fitur Utama | Analisis peta Zi Wei Dou Shu, kalkulasi Numerologi (Life Path), artikel basis pengetahuan astrologi |
| Target Pengguna | Peminat metafisika Timur tradisional; pengguna bahasa Tionghoa Tradisional dan multibahasa |
| Sistem Pengetahuan | Zi Wei: aliran Lu Binzhao (Zhongzhou) San-He + Si-Hua; Numerologi: sistem Pythagoras |
| Mesin Peta | iztro v3.x (open source, dapat diverifikasi di GitHub) |
| Model AI | Claude Sonnet 4.5 (Anthropic) — generasi berbantuan, ditinjau manusia |
| Bahasa Didukung | 10 bahasa (zh-TW / zh-CN / en / ja / ko / es / vi / th / ms / id) |
| Infrastruktur | Cloudflare Pages + Workers + Supabase (PostgreSQL) |
2. Infrastruktur Basis Pengetahuan
2.1 Skala
| KB Zi Wei Dou Shu | 225+ file, 88 indeks, struktur 8 jilid (01-dasar ~ 09-lanjutan) |
| KB Numerologi | 141 file ~56.544 baris |
| Total KB | 2.758 file ~1.537.711+ baris teks terstruktur |
| Indeks Vektor | Basis data vektor semantik berskala besar (Qdrant) |
| Graf Pengetahuan | Graf pengetahuan (Neo4j, bintang × istana × Si-Hua) |
2.2 Peringkat Mutu Pengetahuan
- Grade A: literatur primer (Lu Binzhao / Pythagoras) — dapat dikutip langsung
- Grade B: mesin sumber terbuka iztro / data web publik multi-sumber (distilasi) — dapat dikutip dengan verifikasi silang
- Grade C: forum / blog — hanya dapat dikutip dengan verifikasi silang Grade A/B
2.3 Mekanisme Integritas Aliran
Pandangan Si-Hua non-Lu-Binzhao diberi label ⚠️ Non-Lu-Binzhao. KB memelihara daftar blokir untuk mencegah perayapan ulang sumber yang terdeteksi prompt injection.
3. Pipeline Berbantuan AI
3.1 Pipeline Analisis Peta dalam Bahasa Jelas
3.2 Pipeline Produksi Artikel
4. Mekanisme Jaminan Mutu
4.1 Model Mutu Loop Tertutup Enam Lapis
- L1 Lapis Output: 232 validasi per artikel; kegagalan memblokir publikasi.
- L2 Lapis Kesalahan: log kegagalan → ≥3 kekambuhan mengajukan aturan baru otomatis.
- L3 Lapis Kinerja: umpan balik trafik GA4 + Cloudflare mengidentifikasi artikel yang perlu disegarkan.
- L4 Lapis Meta-Aturan: rule-audit.js mengaudit sistem aturan sendiri, memensiunkan aturan tak terpicu dalam 90 hari.
- L5 Lapis Lingkungan: header keamanan, kerentanan dependensi, dan masa kedaluwarsa kredensial dipantau otomatis.
- L6 Lapis Strategi: tinjauan strategis triwulanan terhadap lanskap kompetitif.
4.2 Metrik Mutu Utama
| Aturan Keras Astrologi | 117 (41 aturan Empat Transformasi + 49 aturan pola + 17 indikator inti + 10 meta-aturan) |
| Modul Editorial | 19 paket keterampilan (prompts/skills/) |
| Kamus Terminologi | 221 istilah × 9 bahasa (glossary.json) |
| Pustaka Kasus | 435 kasus astrologi (dianonimkan + terverifikasi) |
| Validasi Otomatis | Checklist 232 butir (sop-checklist.md) |
5. Batasan dan Ambang yang Diketahui
5.1 Batasan Pengetahuan
- Hanya aliran Lu Binzhao yang digunakan; aliran lain (Wang Tingzhi, Zheng Muting, dll.) tidak sepenuhnya tercakup.
- Numerologi hanya menghitung angka yang dapat diturunkan dari tanggal lahir; sistem numerologi berbasis nama tidak didukung.
- Ramalan tahunan kedaluwarsa seiring waktu; KB tidak menyimpan prediksi yang memuat tahun tertentu.
- Analisis sinastri hubungan adalah fitur yang direncanakan dan belum diluncurkan.
5.2 Risiko Asistensi AI yang Diketahui
- Risiko Halusinasi: dimitigasi oleh 117 aturan keras + penandaan kutipan KB, namun tidak dapat dihapus sepenuhnya.
- Bias Aliran: data pelatihan cenderung ke aliran Lu Binzhao; aliran lain diberi label sistematis tetapi tidak dibahas secara mendalam.
- Mutu Bahasa: terjemahan 10 bahasa menggunakan AI yang ditinjau non-penutur asli; mutu bervariasi.
- Kekinian: batas pengetahuan model AI mungkin tertinggal dari riset terbaru; pembaruan KB manual menjadi penyeimbang.
6. Tata Kelola dan Pembaruan
| Pengawas Manusia | mychenan (pemilik situs, peneliti astrologi) |
| Kolaborator AI | Claude Sonnet 4.5 × 20 (Anthropic) |
| Audit Gabungan | Per batch: pemungutan suara Anthropic + OpenAI + Google Gemini governance benchmarks |
| Siklus Pembaruan | Kartu Sistem diperbarui triwulanan; perubahan kapabilitas besar memicu pembaruan segera. |
| Dokumen Publik | Metodologi / Kartu Model / Benchmark / Pernyataan Etika |
| Dokumen Tata Kelola | Dokumen Tata Kelola / Log Transparansi |
Kontak: Instagram @mychenan
Standar eksternal dan sumber primer
Sumber primer berikut menjadi acuan halaman ini. Ini adalah tolok ukur, bukan dukungan pihak ketiga terhadap situs ini.
Current Machine Audit Snapshot
This block uses only traceable local audit data. No unsupported metrics or model claims are added.
- data/state-machine/i18n-parity.json: 8,036 parent URLs, 7,976 articles.
- data/kb-machine-audit.json: 3,238 source files, 0 missing coverage, 0 orphan chunks.
- data/discovery-surface-audit.json: 0 errors, 0 warnings.
- data/sla-report.json: critical / 5 critical, 0 warnings.
Content Maintenance And Update Decision
This block makes governance-page content machine-checkable: every page must disclose its source artifacts, related pages, and the gate that reports update needs.
Update Decision
This is not static copy. When source artifacts, related policies, public metrics, or generators change, AI Ops reports evidence and an AI agent decides whether the page needs edits.
Human Boundary
Systems detect, report, and preserve machine-readable evidence. Codex/Claude agents perform final judgment and repair.
Verification Command
node scripts/verify-trust-pages.js --check
data/state-machine/model-card.jsondata/state-machine/public-bench.jsondata/public-claim-registry.json- Related governance pages: Data Governance · Transparency Report · Research · model-card · methodology
- Update flow:
npm run update:trust-pages→npm run test:trust
Verifiable Evidence Layer
This block is not a narrative claim. Each core assertion has a claim id, source JSON, hash, and a repeatable verification command. Public pages disclose governance evidence without exposing source code, secrets, private data, or exploitable attack details.
| Claim ID | Verifiable value | Status | Owner | Source and verification |
|---|---|---|---|---|
| claim.public-url-manifest.indexable-count Public URL and canonical inventory |
38,965 indexable URLs | verified | sitewide | node scripts/generate-public-evidence-manifest.js --dry |
| claim.trust-pages.audit-pass-rate Trust page machine audit |
180/180 pass | verified | sitewide | node scripts/verify-trust-pages.js --check |
| claim.discovery-surface.zero-errors AI discovery surface audit |
{"errors":0,"warnings":0} | verified | sitewide | node scripts/verify-discovery-surface.js |
| claim.structured-data.jsonld-errors JSON-LD / structured data audit |
{"structured_data_invalid_files":0,"breadcrumb_count":28274,"faq_count":27506,"dataset_count":30,"article_count":27406} | verified | sitewide | node scripts/site-machine-audit.js |
| claim.status.sla-state Status page SLA source |
critical / 5 critical, 0 warnings | verified | sitewide | node scripts/generate-status-page.js |
| claim.provider-alignment.openai-anthropic-gemini OpenAI / Anthropic / Google Gemini benchmark alignment |
benchmark alignment only unless code/config evidence exists | verified | sitewide | node scripts/verify-public-evidence.js --check |
| claim.transparency-report.sha256 Transparency report SHA-256 anchor |
{"report":"transparency/report-2026-Q3.json","sha256":"47b09e2ca4e8b8fe9dffdfaccef3b11212de9ee3a8a14badca8044e2481203c5"} | verified | sitewide | node scripts/update-transparency-current-data.js |
| claim.release-integrity.gpg-signing GPG signing status |
GPG signing configured locally; GitHub verification pending | github_verification_pending | sitewide | gpg --list-secret-keys --keyid-format=long && git log -1 --show-signature |
| claim.system-card.production-model-evidence Production model setting source scan |
{"productionModelEvidence":[]} | verified | system-card | node scripts/verify-public-evidence.js --check |
| claim.system-card.quality-gates System card machine quality gates |
{"trustPages":{"pages":180,"pass":180,"fail":0},"discovery":{"errors":0,"warnings":0},"structuredData":{"structured_data_invalid_files":0,"breadcrumb_count":28274,"faq_count":27506 | verified | system-card | node scripts/verify-trust-pages.js --check && node scripts/verify-discovery-surface.js |
| claim.system-card.kb-tm-lineage KB / TM source lineage |
{"kb":{"source_files":3238,"chunks":32724,"chunked_source_files":3238,"excluded_source_files":0,"missing_chunk_coverage":0,"orphan_chunks":0},"tm":{"total_entries":789031,"counts": | verified | system-card | node scripts/verify-public-evidence.js --check |
| claim.system-card.provider-benchmark-scope Provider benchmark scope separated from production usage |
{"benchmarkProviders":["OpenAI","Anthropic","Google Gemini"],"productionModelEvidence":[]} | verified | system-card | node scripts/verify-public-evidence.js --check |
System Card V2.0: Technical Transparency Layer
This layer publishes the technical governance evidence that can be safely disclosed: architecture, data sources, AI-use boundaries, quality gates, release integrity, and provider alignment. Source code, secrets, exploitable attack details, and private data remain out of scope.
Public architecture
Cloudflare Pages/Workers, R2/D1/KV/Pagefind, and local generation scripts form the public-site and governance publication chain. Public pages disclose behavior, state, and traceable sources, not secrets or internal permissions.
AI-use disclosure
AI-assisted workflows are used for knowledge-base retrieval, cross-checking, and error detection. Governance documents are benchmarked against OpenAI, Anthropic, and Google Gemini public frameworks. Production model usage is disclosed only when code/config evidence exists.
Quality and safety gates
Governance page audit 180/180 passing, JSON-LD errors 0, discovery-surface errors 0. Status pages report critical / 5 critical, 0 warnings as-is.
Data traceability
Knowledge base 32,724 chunks, TM 789,031 entries, AI answer-ready 7,976/7,976. Public metrics trace to data/state-machine/*, data/*audit*.json, and transparency reports.
| Governance area | OpenAI | Anthropic | Google Gemini | Starnum implementation evidence |
|---|---|---|---|---|
| Model/system-card disclosure | OpenAI models + safety docs | Claude model docs + system/model cards | Gemini model docs + safety settings | system-card, model-card, methodology, benchmark, transparency-log |
| Safety evaluation and use boundaries | Safety best practices / deployment checklist | Responsible Scaling / safety policy | Gemini safety controls / policy | AI safety, acceptable-use, ethics, risk-boundary copy, crawler policy audit |
| Data governance | Data controls / privacy controls | privacy and data handling docs | Gemini API data governance references | privacy, ai-data-governance, KB/TM source tracking, SHA-256 hashes |
| Monitoring and release | production checklist / eval discipline | system-card transparency discipline | model/version documentation discipline | deploy.js, status.html, SLA report, trust-pages-machine-audit, sitemap/hreflang audits |
- Sources: data/state-machine/model-card.json, public-bench.json, trust-pages.json, security-headers.json.
- Sources: data/trust-pages-machine-audit.json, data/discovery-surface-audit.json, data/ai-answer-readiness-audit.json.
- Sources: data/kb-machine-audit.json, data/tm/quality-audit-report.json, data/sla-report.json.
- Official benchmark docs checked: 2026-07-30; links are listed in the OpenAI / Anthropic / Google Gemini alignment table.
The V2.0 goal is not more claims; it separates implemented controls from planned controls. Production usage, benchmark alignment, status exceptions, GPG signing, and SLA breaches are disclosed from source data.
Release Integrity And GPG
GPG signing configured locally. signingkey=0934DFA0EDA6363A. GitHub verification pending until the public key upload and Verified badge are confirmed.
OpenAI / Anthropic / Google Gemini Alignment
The governance surface is benchmarked against the three public frameworks: model docs, system/model cards, safety evaluation, data governance, and use policies. This is benchmark alignment, not a claim that every provider is active in production inference. Official docs checked: 2026-07-30
| Provider | Governance focus | Starnum disclosure | Official source |
|---|---|---|---|
| OpenAI | Model documentation, latest model notes, safety best practices, and data controls. | No verifiable production model setting was found in the production code scan; providers are listed as governance benchmarks. | https://platform.openai.com/docs/models |
| Anthropic | Claude model documentation, system/model cards, Responsible Scaling, and safety policy. | No verifiable production model setting was found in the production code scan; providers are listed as governance benchmarks. | https://docs.anthropic.com/en/docs/about-claude/models |
| Google Gemini | Gemini API model documentation, safety settings, data governance, and platform policy. | No verifiable production model setting was found in the production code scan; providers are listed as governance benchmarks. | https://ai.google.dev/gemini-api/docs/models |