Bibliometric Tools
The most widely used measure of academic impact. Enter citation counts or fetch live data from OpenAlex. Calculates H-Index, G-Index, i10-Index with full citation breakdown.
Corrects the H-index's blind spot for breakthrough papers. The largest g where your top g papers together have g² or more total citations.
Count your papers with 10+ citations — Google Scholar's simplest impact metric. Includes field-specific benchmarks and percentile ranking.
Career & Research
Model your expected H-Index, total papers, and citation count 5, 10, and 20 years from now — adjusted for field and career stage.
Plan your doctorate: milestone scheduling, completion probability, and time-to-degree by field and country.
Track your progress toward tenure: publications, grants, teaching, and service benchmarks by field.
Impact & Risk Analysis
Estimate your paper's citation trajectory based on field, journal tier, and open-access status. 5-year forecast with confidence intervals.
AI existential risk estimator. Combines superintelligence arrival, alignment failure, and malicious use probabilities into a composite risk estimate.
Which Metric Should I Use?
H-Index
Best for general benchmarking. Balances quantity (number of papers) and quality (citations). Used by most universities for tenure and promotion decisions.
G-Index
Better when you have highly-cited breakthrough papers. Gives proper credit to a paper with 2,000 citations vs. one with 20 — which H-index treats identically above threshold.
i10-Index
Simplest: just counts papers with 10+ citations. Used by Google Scholar. Transparent and easy to understand, but less nuanced than H or G.
Career Projection
Use for planning: grant applications, tenure timelines, benchmarking against field averages. Adjusts for field-specific citation norms and career stage.
From the Blog
Benchmarks by field and career stage — from PhD students to Nobel laureates.
When does the G-index give you a better picture of your research impact?
A researcher's guide to AI risk probability — the math behind existential risk estimates.