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
7Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2239426162
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Chen Wang (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Productivity: 1 paper
Claim outcomes
- Other: 1 paper
- Error Rate: 1 paper
- Output Quality: 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 |
|---|---|---|---|
| A lightweight 'FutureBoosting' pipeline that uses a frozen time‑series foundation model to generate forecasted features and feeds them into an interpretable regressor cuts electricity price forecasting MAE by more than 30% across multiple markets. The approach is plug‑and‑play, computationally modest, and retains model interpretability, making it attractive for market participants seeking better bids and risk management.arxiv | Chen Wang provider id |
2026-03-06 | 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.