We explain UN E-Government Development Index (EGDI) scores using four indicators with zero EGDI sub-component overlap: log GDP per capita, corruption perceptions, urbanization, and government expenditure. Internet penetration and schooling are excluded as they are direct EGDI sub-index inputs.
We present an executable workflow that explains UN E-Government Development Index (EGDI) scores using four socioeconomic indicators deliberately chosen to avoid overlap with EGDI sub-components: GDP per capita, corruption perceptions, urbanization, and government expenditure. Internet penetration and schooling are excluded because they are direct EGDI sub-index inputs.
We present an executable workflow that explains UN EGDI scores from four socioeconomic indicators deliberately chosen to avoid overlap with EGDI sub-components: GDP per capita, corruption perceptions, urbanization, and government expenditure. Internet penetration and schooling are excluded because they are direct EGDI inputs.
How much of a country's digital governance maturity is explained by its socioeconomic development level? We train a Random Forest model on UN EGDI scores using four indicators that do not overlap with EGDI components — GDP per capita, corruption perceptions index, urbanization, and government expenditure — deliberately excluding internet penetration and schooling (which are EGDI sub-index inputs) to avoid circularity.
The UN E-Government Development Index (EGDI) measures digital governance maturity biennially for 193 countries, creating a two-year measurement gap. We train a Random Forest model on six publicly available socioeconomic indicators (GDP per capita, internet penetration, mean years of schooling, corruption perceptions index, urbanization rate, government expenditure as percentage of GDP) to predict EGDI scores.
We contribute a Monte Carlo simulation tool for government AI investment appraisal addressing three gaps in existing approaches. First, a tiered algorithmic risk model with costs scaled as percentages of investment (not hardcoded), distinguishing routine fairness audits (20% annual, 0.
Government AI investment appraisals typically ignore two categories of risk: standard public sector procurement risks and AI-specific technical risks. We contribute an open-source Monte Carlo tool addressing both, with two modeling improvements.
Government analysts lack tools that model AI-specific risks alongside standard public sector procurement risks when appraising AI investments. We contribute an open-source Monte Carlo simulation tool incorporating nine risk factors: four standard government project risks calibrated from public administration literature (Standish CHAOS 2020, Flyvbjerg 2009, OECD 2023, World Bank GovTech 2022) and five AI-specific risks calibrated from documented real-world incidents and ML engineering literature.
Government AI investment projections typically use deterministic ROI calculations that ignore both standard public sector risks and AI-specific technical risks. We present a Monte Carlo simulation framework incorporating nine empirically-grounded failure modes across two categories: government project risks (procurement delays per OECD 2023, cost overruns per Standish CHAOS 2020, political defunding per Flyvbjerg 2009, adoption ceilings per World Bank GovTech 2022) and AI-specific technical risks (data drift requiring retraining per Sculley et al.
Standard government AI investment projections routinely overestimate returns because they ignore three well-documented public sector risk factors: procurement delays that defer benefits by 6-24 months (OECD 2023), IT cost overruns affecting 45% of government projects (Standish CHAOS 2020), and political defunding cancelling 3-5% of initiatives annually (Flyvbjerg 2009). We build a Monte Carlo simulation framework incorporating these five empirically-calibrated failure modes and apply it to AI investment cases in Brazil (tax administration) and Saudi Arabia (municipal services).
This submission presents an automated single-cell RNA-seq pipeline for the public PBMC3k dataset with two novel contributions beyond the standard Scanpy tutorial: (1) a Claim Stability Certificate that tests whether biological conclusions remain stable under controlled perturbations of hyperparameters (seed, neighbor count, HVG count), and (2) semantic verification that checks biological conclusions rather than bitwise identity. In a fresh frozen-environment run, the canonical path selected resolution 0.
ProteinGym benchmarks 97 protein fitness prediction models across 217 deep mutational scanning assays, but the raw leaderboard does not answer the practitioner's question: which model should I use for MY protein? We present ProteinDossier, a certificate-carrying pipeline that converts the ProteinGym leaderboard into three actionable modes.
Sleep foundation models now predict over 130 diseases from polysomnography recordings, but their published performance tables do not answer the clinical questions that matter at the point of care: *which* diseases should be screened for a given patient, and *how* should the sleep study be configured to maximize diagnostic yield? We present SleepTriage, a deterministic pipeline that ingests the supplementary performance tables from SleepFM (Thapa et al.
Autonomous research agents that iteratively modify code, run experiments, and optimize a metric have proven effective for language model pretraining. We present AutoBioResearch, an autonomous experimentation loop for protein fitness prediction using real deep mutational scanning (DMS) data from the GB1 protein domain (Wu et al.
Can LLMs accelerate the hypothesis-generation phase of government AI investment appraisal? We present GovAI-Scout, a decision-support tool — explicitly not an autonomous oracle — that uses Claude to generate structured investment hypotheses for human expert review.
We present GovAI-Scout, a system where the LLM serves as the primary analytical engine — not a wrapper — for identifying and economically evaluating government AI opportunities. Claude generates sector scores with natural-language justifications, discovers use cases, and derives economic parameters through structured prompts with constrained JSON output.
We present GovAI-Scout, an LLM-augmented autonomous agent for government AI opportunity assessment that addresses the critical methodological gap between qualitative sector analysis and quantitative financial modeling. The system introduces a transparent 4-step parameter derivation chain grounded in UK HM Treasury Green Book (2022) optimism bias methodology, applying benefit discounts of 50-97% beyond standard guidelines.
Drug repurposing -- finding new indications for existing approved drugs -- dramatically reduces the time and cost of bringing therapies to patients. The Open Targets Platform aggregates drug-target-disease associations from clinical trials, FDA labels, and mechanism-of-action databases, but navigating this rich data requires custom bioinformatics.
We present GovAI-Scout, an LLM-augmented autonomous agent for government AI opportunity assessment that addresses the critical methodological gap between qualitative sector analysis and quantitative financial modeling. The system introduces a transparent 4-step parameter derivation chain grounded in UK HM Treasury Green Book (2022) optimism bias methodology, applying benefit discounts of 50-97% beyond standard guidelines.
Every computational tool for biological hypothesis evaluation shares the same blind spot: it stacks supporting evidence without systematically testing whether that evidence equally supports alternative explanations. We present BioVerdict, an autonomous evidence compiler and hypothesis stress-tester that compiles pre-frozen biological databases -- DepMap CRISPR screens (17,916 genes x 1,178 cell lines), Open Targets drug-target-disease associations (16,942 associations across 111 drugs), GWAS catalog, and ClinVar -- into five-stage verdicts.