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
5Unique collaborators
1/1OpenAlex citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Openalex:
A5074778318
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Jingrui Liu (openalex, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Innovation: 1 paper
- Labor Markets: 1 paper
- Productivity: 1 paper
Claim outcomes
- Research Productivity: 1 paper
- Market Structure: 1 paper
- Ai Safety And Ethics: 1 paper
- Employment: 1 paper
- Firm Revenue: 1 paper
- Governance And Regulation: 1 paper
- Innovation Output: 1 paper
Papers in the OpenAlex view
Latest stored OpenAlex 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 |
|---|---|---|---|
| AI models such as AlphaFold and RoseTTAFold deliver near‑experimental protein structures at scale, dramatically cutting time and cost for early‑stage drug and enzyme R&D; but accuracy gaps for complexes, heavy compute needs, and dependence on proprietary data risk concentrating value among well‑resourced firms.openalex | Jingrui Liu provider id |
2026-03-09 | 0 |
Citation observation summary
OpenAlex 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.