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:
2364453658
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Kangxing Dong (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Innovation: 1 paper
- Org Design: 1 paper
- Productivity: 1 paper
- Skills Training: 1 paper
Claim outcomes
- Adoption Rate: 1 paper
- Research Productivity: 1 paper
- Firm Productivity: 1 paper
- Organizational Efficiency: 1 paper
- Other: 1 paper
- Governance And Regulation: 1 paper
- Market Structure: 1 paper
- Regulatory Compliance: 1 paper
- Task Completion Time: 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 |
|---|---|---|---|
| Digital twins can deliver large productivity and sustainability gains in advanced construction pilots but those wins are not yet industry‑wide; scaling value will depend on interoperability standards, new contracting and data‑governance arrangements, and investment in skills and delivery models.openalex | Kangxing Dong provider id |
2026-03-09 | 1 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 1 cumulative citations. This is a coverage summary, not an author score or h-index.