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:
98510344
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- M. T. Kurbucz (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Governance: 1 paper
- Human Ai Collab: 1 paper
Claim outcomes
- Decision Quality: 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 |
|---|---|---|---|
| Turning classifier probabilities into hard labels can arbitrarily twist downstream estimates and make advertised confidence intervals misleading; the paper derives an explicit, estimable operator that predicts how coarsening biases coefficients and erodes coverage direction-by-direction, enabling practitioners to audit the damage before reporting results.arxiv | M. T. Kurbucz provider id |
2026-08-12 | 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.