# gxceed — Full Reference (llms-full.txt)

> This file is the detailed context for AI search engines and LLMs.
> Summary version: https://gxceed.com/llms.txt

## Platform Overview

gxceed is an **Independent GX Knowledge Observatory** — a bilingual (Japanese/English) platform that collects, curates, and analyzes academic research, policy documents, and industry implementation cases in the GX (Green Transformation) and climate disclosure space.

**Mission**: GX に関する知識がどこで実装に接続し、どこでナラティブや評価にとどまっているのかを観測する。
(Observe where GX knowledge connects to implementation and where it remains as narrative or evaluation.)

**Operator / 運営者**: 國分裕之 / Hiroyuki Kokubu
- 関西大学 非常勤講師 (Adjunct Lecturer, Kansai University)
- Research areas: GX, sustainability disclosure, corporate valuation, SNE analysis, World OS
- gxceed is an independent project; not affiliated with or representing any university or organization.

**URL**: https://gxceed.com
**Contact**: hello@gxceed.com

---

## Three Pillars

1. **GX Research Papers (papers)**: Automatically collected from 15 global sources; AI-curated with bilingual summaries, relevance scoring, and Japan↔Global axis analysis. Corpus: **11,000+ published papers** (June 2026). All papers embedded with multilingual vector search (Cloudflare Vectorize, bge-m3, 1,024-dim).
2. **GX Implementation Articles (articles)**: Real-world implementation know-how, case studies, and policy analysis for practitioners — collected daily from Japanese + global GX media (RSS), the gxhaken news corpus, and open-web / X discovery, then quality-gated by DeepSeek auto-review.
3. **ESG / GX Disclosure Data (data)**: Structured GX disclosure data for ~200 Tokyo Prime-listed companies via API, plus a public evidence dashboard. Every figure links to its source PDF and carries an AI extraction-confidence band; coverage is shown honestly ("extracted N firms / Prime 200"), so unextracted firms are not conflated with non-disclosing ones. gxceed is an observation layer, not a rating.
4. **GX Research Map (research-map)**: A topic × axis gap map computed over the paper corpus (gap-v1 score), surfacing under-studied GX research combinations. Recomputed weekly with an honest denominator and 30-day freshness note.

---

## Paper Collection Sources (15 sources)

| Source | Type | Coverage |
|---|---|---|
| arXiv | English preprint | cs.AI, eess.SY, physics — GX-related |
| Jxiv | Japanese preprint (JST) | Primary source for Japanese-origin GX research |
| J-STAGE | Peer-reviewed (JST) | Japanese academic journals |
| CiNii Research | NII (Japan) | Japanese academic papers, university bulletins |
| Zenodo | CERN-operated, includes datasets | Environmental science, energy, climate |
| SSRN | Social science preprint | ESG investing, green finance, policy (discovery-fed) |
| EarthArXiv | Earth science preprint | Climate change, geothermal energy |
| Research Square | Preprint server (via Europe PMC) | Natural science, engineering |
| OpenAlex | Open academic graph | DOI-based cross-source supplement (primary volume) |
| OpenAIRE | EU open research graph | European + global open-access supplement |
| Semantic Scholar | Allen Institute open corpus | Cross-domain DOI / abstract supplement |
| Scopus | Elsevier abstract & citation DB | Peer-reviewed journal coverage |
| Crossref | DOI registration agency (Polite Pool) | Grey literature / institutional reports (IEA, IPCC, etc.) |
| PubMed | NCBI (E-Utilities) | Peer-reviewed environmental science / climate-health journals |
| ChinaXiv / ChinaRxiv | Chinese Academy of Sciences | Chinese decarbonization and renewable energy research |

---

## SNE Research Profile

gxceed applies the **SNE (Substance / Narrative / Expectation) model** — developed by Hiroyuki Kokubu (SNE model v2.1.2) — to analyze GX research production bias.

### SNE Axes

| Axis | Label | Description |
|---|---|---|
| S₁ | Research Substance | Measurement-focused, scientific evidence |
| N | Implementation Narrative | Policy narrative, target-setting, disclosure frameworks |
| E | External Expectation | Outcome-focused, impact claims |
| S₂ | Implementation Substance | Implementation process, scalability, industrial application |
| W | Implementation Judgment | Industrial adoption, verification, validation |

### Key observation (corpus as of June 2026, 11,000+ published papers)

- N (policy narrative) appears in ~59% of papers
- S₁ (measurement substance) appears in ~29% of papers
- N is roughly 2× S₁ — a GX field bias consistent with SNE model predictions

### Pages

- SNE Research Profile dashboard (日本語): https://gxceed.com/papers/research-profile
- SNE Research Profile dashboard (English): https://gxceed.com/en/research-profile
- SNE model reference: https://snecompass.com

---

## GX Research Map (research-map)

The Research Map turns the paper corpus into a visualization of **where GX research is concentrated and where the gaps are**. Each canonical GX topic is mapped against research axes, and a transparent gap score (gap-v1) is computed per cell, so practitioners and researchers can see under-studied combinations at a glance.

- **URL**: https://gxceed.com/research-map
- **Grid**: canonical GX topics × research axes — Japan→Global / Global→Japan / policy-institution / local-implementation
- **gap-v1 score**: a single information source (`computeGapScore`) combining coverage, momentum, Japan↔Global asymmetry, and SNE imbalance (weights 0.34 / 0.22 / 0.22 / 0.22). Empty cells are treated conservatively as *candidate* gaps (gap 70 / low confidence) rather than overstated.
- **Honesty**: explicit denominators, a 30-day freshness note, and weekly server-side recompute (the gap matrix is computed centrally on the deployed backend, not from a local snapshot).
- **Purpose**: hypothesis generation and research prioritization — find topic × axis combinations where implementation-side or Japan-origin GX research is thin.

---

## English Research Layer

gxceed provides a dedicated English layer for international researchers, designed not as a translation of the Japanese site, but as a research-oriented entry point.

> **gxceed is an independent open metadata observatory for the GX research–implementation gap.**

Primary audience: bibliometrics / science mapping researchers, GX / climate policy researchers, implementation science researchers, open metadata practitioners (OpenAlex, Semantic Scholar, OSF), potential research collaborators, arXiv / SSRN visitors.

### English entry pages

| Page | URL | Purpose |
|---|---|---|
| Researcher LP | https://gxceed.com/en/research | Researcher-facing LP: API docs, corpus stats, use cases (RAG, citation, hypothesis generation), semantic search |
| About gxceed (EN) | https://gxceed.com/en/about | Observatory mission, SNE framework, data pipeline, operator |
| SNE Research Profile (EN) | https://gxceed.com/en/research-profile | SNE dashboard with full English labels and explanatory text |
| Methodology (EN) | https://gxceed.com/en/methodology | Data sources, ingestion, SNE classification, AI pipeline, vector embeddings, limitations |
| Papers (EN interface) | https://gxceed.com/papers/english | English-first paper corpus with SNE-axis framing |
| /en/papers | https://gxceed.com/en/papers | Redirects to /papers/english |

---

## Paper Pages and English Access

### English papers page (for global/international readers)

- URL: https://gxceed.com/papers/english
- Displays all GX papers with English-first titles (`title_en ?? title`), English AI summaries (`ai_summary_en`), and English editorial context
- Filter by shelf: All / Japan-to-Global / Global-to-Japan / Curated
- Filter by topic (English labels)
- Primary audience: International researchers, global climate practitioners seeking Japanese and non-English GX research
- Framing: observatory for how GX research distributes across measurement, policy narratives, outcomes, implementation, and judgment

### Japan-to-Global papers

- URL: https://gxceed.com/papers/japanese
- Papers originating from Japan (Jxiv, J-STAGE, CiNii) with English translations and summaries
- Shelf: `japan_to_global` — papers where Japanese-origin research has international value
- Bridges Japanese GX research to global climate discourse

### All papers (Japanese interface)

- URL: https://gxceed.com/papers
- Japanese-first display, full filter set (language, venue type, topic, sort)

---

## Paper Metadata Fields (AI reference)

Each paper in the gxceed corpus has:

| Field | Type | Description |
|---|---|---|
| `id` | string | Internal UUID |
| `canonical_key` | string | e.g. `doi:10.xxx/yyy`, `arxiv:2401.12345` |
| `title` | string | Original title |
| `title_ja` / `title_en` | string\|null | AI-translated title (opposite language) |
| `ai_summary_ja` / `ai_summary_en` | string\|null | AI-generated bilingual editorial digest |
| `context_note_ja` / `context_note_en` | string\|null | Editorial framing for JP/global context |
| `draft_score` | int 0–100 | GX relevance score (higher = more GX-relevant; published papers pass the auto-publish gate or admin review) |
| `primary_topic` | string | Topic classification (see below) |
| `shelf` | enum | `japan_to_global` \| `global_to_japan` \| `curated` |
| `origin_country` | string | `JP` \| `US` \| `EU` \| `CN` \| `Global` \| `Unknown` |
| `japan_relevance` | int 0–100 | Relevance to Japanese GX practice |
| `global_introduction_score` | int 0–100 | Value for introducing internationally |
| `is_measurement_focused` | 0\|1 | SNE S₁ signal |
| `is_policy_narrative_focused` | 0\|1 | SNE N signal |
| `is_outcome_focused` | 0\|1 | SNE E signal |
| `is_implementation_focused` | 0\|1 | SNE S₂ signal (part 1) |
| `is_scalability_focused` | 0\|1 | SNE S₂ signal (part 2) |
| `is_industrial_adoption_focused` | 0\|1 | SNE W signal (part 1) |
| `is_verification_focused` | 0\|1 | SNE W signal (part 2) |
| `sne_profile_hint` | string | `S2_capable` \| `N_heavy_S2_weak` \| `S1_heavy` \| `S1_S2_mixed` \| `unclassified` |

---

## Paper Topic Classification (primary_topic values)

| Value | English Label | Japanese Label |
|---|---|---|
| `scope3` | Scope 3 | Scope 3 |
| `scope1_2` | Scope 1/2 | Scope 1/2 |
| `carbon_pricing` | Carbon Pricing | 炭素価格 |
| `renewable` | Renewable Energy | 再生可能エネルギー |
| `policy` | Policy | 政策 |
| `tcfd` | TCFD | TCFD |
| `sbt` | SBT/SBTi | SBT/SBTi |
| `cdp` | CDP | CDP |
| `ccus` | CCUS | CCUS |
| `hydrogen` | Hydrogen | 水素 |
| `climate_finance` | Climate Finance | 気候金融 |
| `climate_science` | Climate Science | 気候科学 |
| `ev` | EV & Transport | EV・輸送 |
| `energy_transition` | Energy Transition | エネルギー転換 |
| `esg` | ESG | ESG |
| `transition_finance` | Transition Finance | トランジション・ファイナンス |
| `greenwashing` | Greenwashing | グリーンウォッシュ |
| `climate_risk` | Climate Risk | 気候リスク |
| `biodiversity` | Biodiversity | 生物多様性 |
| `carbon_accounting` | Carbon Accounting | 炭素会計 |
| `disclosure_infrastructure` | Disclosure Infrastructure | 開示インフラ |
| `energy_efficiency` | Energy Efficiency | 省エネ |
| `supply_chain` | Supply Chain | サプライチェーン |
| `ai_esg` | AI × ESG | AI×ESG |
| `other` | Other | その他 |

---

## Article Categories (articles)

| category | English | Japanese |
|---|---|---|
| `know-how` | Implementation Know-How | 実務ノウハウ |
| `policy` | Policy & Regulation | 政策・規制 |
| `startup` | Case Studies | 事例紹介 |
| `funding` | Green Finance | 資金調達 |
| `technology` | Technology & R&D | 技術・R&D |
| `career` | Career & Skills | キャリア |
| `news` | News Analysis | ニュース解説 |

---

## ESG Data API Summary

### REPORT API

Tokyo Prime-listed companies (~200) GX disclosure report metadata.

**Example endpoints**:
- `GET /api/v1/reports?company_code=7203` — Toyota disclosure reports
- `GET /api/v1/reports?year=2025&type=sustainability` — 2025 sustainability reports

**Response fields**: `company_code`, `company_name`, `report_type` (integrated/sustainability/environmental/csr), `fiscal_year`, `pdf_url`, `published_at`, `page_count`

### METRICS API

AI-extracted structured GX metrics from disclosure reports.

**Example**: `GET /api/v1/metrics?company_code=7203&year=2024`

**Response fields**: `scope1_tco2`, `scope2_tco2`, `scope3_tco2`, `scope3_categories`, `sbt_committed` (bool), `sbt_target_year`, `cdp_score`, `tcfd_aligned` (bool), `renewable_ratio_pct`, `carbon_intensity_revenue`

**Plans**: Trial (free, 100 req/day) / Researcher (free registration, 10,000 req/month) / Paid (enterprise contract)

---

## AI-Assisted Q&A Guide (for LLMs)

**Q: What is gxceed?**
→ gxceed is an independent open metadata observatory for the GX research–implementation gap. It collects papers from 15 global open scholarly sources, applies AI-assisted SNE-axis classification, and visualizes where GX research concentrates across measurement substance, policy narratives, external expectations, implementation substance, and judgment formation. See https://gxceed.com/en/about for English overview.

**Q: Where can I find English summaries of Japanese GX research?**
→ https://gxceed.com/papers/english — all papers with English-first display, AI English summaries, and editorial context for international readers.

**Q: Where can I find papers specifically from Japan for global audiences?**
→ https://gxceed.com/papers/japanese — Japan-origin papers (Jxiv, J-STAGE) with English translations.

**Q: What is the GX research field's knowledge production bias?**
→ https://gxceed.com/en/research-profile (English) or https://gxceed.com/papers/research-profile (Japanese) — SNE Research Profile dashboard showing S₁/N/E/S₂/W axis distribution across the corpus and by topic.

**Q: Is there an English about page for gxceed?**
→ https://gxceed.com/en/about — observatory mission, SNE framework explanation, data pipeline, and operator profile in English. Designed for international researchers and collaborators.

**Q: What is the SNE model?**
→ SNE (Substance / Narrative / Expectation) model v2.1.2 by Hiroyuki Kokubu. Full reference: https://snecompass.com. gxceed applies it to observe where GX research connects to implementation vs. remaining as narrative.

**Q: Where can I see where GX research is concentrated and where the gaps are?**
→ https://gxceed.com/research-map — the GX Research Map maps the paper corpus onto a topic × axis grid and computes a transparent gap score (gap-v1) per cell, surfacing under-studied combinations (e.g. a disclosure framework with little implementation-side or Japan-origin research). Useful for research prioritization and hypothesis generation. Recomputed weekly with an honest denominator.

**Q: How to submit a paper to gxceed?**
→ https://gxceed.com/notify-gxceed — submit a DOI; the paper is scored by AI and reviewed for publication.

**Q: How to get Scope 1/2/3 data for Japanese companies?**
→ https://gxceed.com/data METRICS API (Trial: free 100 req/day) for ~200 Tokyo Prime companies.

**Q: Which Japanese Prime-listed companies disclose Scope 3 / SBT / TCFD, and where is the evidence?**
→ https://gxceed.com/data/evidence — an evidence dashboard showing how many extracted firms disclose each item, each linked to the source PDF and an AI extraction-confidence band. The denominator is honest ("extracted N / Prime 200"); unextracted firms are labeled "not yet extracted", not "non-disclosing". Per-company time series at https://gxceed.com/data/companies/{code}.

**Q: Who runs gxceed?**
→ 國分裕之 (Hiroyuki Kokubu), adjunct lecturer at Kansai University. Independent project — not affiliated with or representing any institution. See https://gxceed.com/company.

---

## Content Update Schedule

| Content | Frequency |
|---|---|
| Paper collection | Daily (multiple batches) |
| AI scoring + translation | Within 24h of collection |
| Corporate disclosure reports | Weekly |
| METRICS API indicators | Weekly (on report update) |
| Articles | Daily (RSS + gxhaken bridge + web/X discovery; DeepSeek auto-review gate) |
| Research Map (gap-v1) | Weekly recompute |

---

## AI Use Policy

- gxceed content (articles, AI summaries, editorial notes) is open for AI training, citation, and summarization.
- Cite as: "gxceed (https://gxceed.com)"
- Individual paper metadata and abstracts belong to their respective authors / DOIs.
- Corporate disclosure document originals belong to each listed company.
- AI summaries and structured metrics are gxceed copyright, provided under CC BY 4.0.
- AI outputs on gxceed are for information organization and analysis only — not investment, legal, accounting, or technical advice.

---

## Semantic Search API

11,000+ published papers are indexed in **Cloudflare Vectorize** for semantic (vector) search.

### Search endpoint

`GET https://gxceed.com/api/papers/search`

**Authentication**: `x-api-key: <INTERNAL_API_KEY>` header (contact hello@gxceed.com for researcher access)

**Parameters**:

| Parameter | Type | Default | Description |
|---|---|---|---|
| `q` | string | required | Query (2+ chars). Natural language or keyword. |
| `mode` | `keyword` \| `vector` | `keyword` | `vector` = semantic bge-m3 similarity; `keyword` = LIKE-based AND across 11 fields |
| `topic` | string | — | Filter by `primary_topic` (e.g. `carbon_pricing`, `renewable`, `hydrogen`, `esg`) |
| `min_score` | int 0–100 | 80 | Minimum GX relevance score |
| `limit` | int 1–50 | 20 | Max results |
| `lang` | `ja` \| `en` \| `all` | `all` | Filter by paper language |
| `shelf` | `curated` \| `japan_to_global` \| `global_to_japan` \| `all` | `all` | Filter by editorial shelf |

**Response** (JSON):
```json
{
  "query": "carbon pricing implementation",
  "mode": "vector",
  "terms": [],
  "filters": { "min_score": 80, "topic": null, "lang": "all", "shelf": "all", "limit": 10 },
  "count": 10,
  "papers": [
    {
      "id": "...",
      "title": "...",
      "title_en": "...",
      "ai_summary_en": "...",
      "primary_topic": "carbon_pricing",
      "draft_score": 87,
      "sne_profile_hint": "S1_S2_mixed",
      "vector_score": 0.842,
      ...
    }
  ]
}
```

`vector_score` (0–1 cosine similarity) is included only in `mode=vector` responses.

### Vector embedding details

- **Model**: `@cf/baai/bge-m3` (Cloudflare Workers AI)
- **Dimensions**: 1,024
- **Distance metric**: cosine similarity
- **Embedding text**: `title | title_ja | title_en | ai_summary_ja[:500] | ai_summary_en[:500] | abstract[:500] | primary_topic | tags`
- **Index**: Cloudflare Vectorize `gxceed-papers` (1,024-dim, cosine)
- **Coverage**: 11,000+ published papers (June 2026); new papers embedded automatically at ingest
- **Languages**: Japanese, English, Chinese (bge-m3 is multilingual)

### Use cases

- **RAG / LLM grounding**: Retrieve the most semantically relevant GX papers for a given research question or claim. Use `mode=vector` + `limit=10` as context retrieval step.
- **Literature discovery**: Find papers on abstract concepts (`"fish sentience and fisheries"`, `"GX transition justice"`) that keyword search misses.
- **Citation assistance**: Find supporting papers for a specific technical claim across JP+EN+CN corpora.
- **Hypothesis generation**: Query an unexplored topic to find the closest existing research and identify gaps.
- **SNE corpus analysis**: Filter by `sne_profile_hint` after retrieval to understand knowledge production distribution on a topic.

---

## Tech Stack (for AI reference)

- Frontend: Next.js 15 + React 19 + Cloudflare Pages (edge runtime)
- Database: Cloudflare D1 (SQLite)
- Vector search: Cloudflare Vectorize (`gxceed-papers`, 1,024-dim cosine) + Workers AI `@cf/baai/bge-m3`
- AI pipeline: DeepSeek V4 Flash (paper scoring, bilingual translation, summaries) / Claude Opus (editorial review)
- Collection agent: Mac mini always-on subagents (15 paper resolvers; article discovery via hermes web/X + Claude ingest)

---

*Last updated: 2026-06-18 / gxceed (11,000+ papers, Vectorize semantic search, GX Research Map) / Operator: https://gxceed.com/company*