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
9Unique collaborators
0/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
144804318
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- S. Watson (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Innovation: 1 paper
Claim outcomes
- Output Quality: 1 paper
- Error Rate: 1 paper
- Other: 1 paper
- Task Allocation: 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 new benchmark reveals frontier LLMs struggle to assemble auditable product carbon footprints: direct single-shot estimates fall within 2× of declared EPD totals 60–77% of the time, but compositional, step-by-step pipelines succeed on only 37–58% and often violate mass conservation, undermining transparency for product-level decarbonization.arxiv | S. Watson provider id |
2026-08-27 | Missing, not zero |
Citation observation summary
Semantic Scholar supplied counts for 0 of 1 papers in this view; 1 are missing.