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
0Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2380689855
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Felicia Nguyen (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Innovation: 1 paper
Claim outcomes
- Task Allocation: 1 paper
- Consumer Welfare: 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 |
|---|---|---|---|
| Products that stand out from their nearest visual look-alikes capture more clicks—but mainly when they already match the query. Visual distinctiveness raises click probability by roughly 5% (and more for well-fitting items) yet contributes less to downstream conversions, implying platforms should target visual diversity among strong candidates rather than uniformly across results.arxiv | Felicia Nguyen provider id |
2026-08-21 | 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.