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: 2025. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2316636776
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Nicholas Kluge Corrêa (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Governance: 1 paper
- Innovation: 1 paper
Claim outcomes
- Other: 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 |
|---|---|---|---|
| Training state-of-the-art AI can demand thousands of GPUs and tonnes of toxic materials: training GPT-4 may require 1,174–8,800 Nvidia A100s, equating to up to seven tonnes of heavy metals. Raising model FLOP utilization and extending GPU lifespans together can cut hardware needs — and material extraction — by as much as 93%, making software efficiency and lifecycle policies essential to sustainable AI.arxiv | Nicholas Kluge Corrêa provider id |
2025-12-03 | 4 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 4 cumulative citations. This is a coverage summary, not an author score or h-index.