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
4Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2279755118
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Marco Ruffini (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Innovation: 1 paper
- Productivity: 1 paper
Claim outcomes
- Output Quality: 1 paper
- Other: 1 paper
- Market Structure: 1 paper
- Firm Revenue: 1 paper
- Governance And Regulation: 1 paper
- Organizational Efficiency: 1 paper
- Research Productivity: 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 |
|---|---|---|---|
| Time-series foundation models trained on low-frequency data stumble on millisecond-scale 5G network dynamics, even after fine-tuning. Firms that collect and pretrain on high-resolution telemetry will have a practical edge in latency-sensitive services, potentially concentrating rents among incumbents.arxiv | Marco Ruffini provider id |
2026-03-17 | 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.