Direction, evidence grade, and study type are AI-generated labels (gpt-5-mini), not human-verified. Syntheses are LLM-written. "Tensions" are machine-detected candidates, not confirmed contradictions. A research-acceleration tool, not peer review.
How this is built →
1Distinct papers
1Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2249702558
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Xuebin Wang (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Innovation: 1 paper
- Productivity: 1 paper
- Skills Training: 1 paper
Claim outcomes
- Ai Safety And Ethics: 1 paper
- Decision Quality: 1 paper
- Organizational Efficiency: 1 paper
- Output Quality: 1 paper
- Adoption Rate: 1 paper
- Governance And Regulation: 1 paper
- Market Structure: 1 paper
- Other: 1 paper
- Skill Acquisition: 1 paper
Papers in the Semantic Scholar view
Latest stored Semantic Scholar author observations only. Citation counts below are from the same provider and are not combined with other services.
Scroll the table horizontally to see every column.
| Paper | Author evidence | Date | Provider citations |
|---|---|---|---|
| Advances in AI promise a 'digital frontier' for Earth observation, but operational value hinges on physics-aware modeling, rigorous evaluation, and costly long-term data and maintenance investments; without governance and lifecycle planning, accuracy gains alone won’t deliver reliable decision-ready products.openalex | Xuebin Wang provider id |
2026-08-12 | 0 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 0 cumulative citations. This is a coverage summary, not an author score or h-index.