input stringclasses 10
values | expected_output stringclasses 10
values | model_output stringclasses 10
values |
|---|---|---|
The leading structural driver of HIV transmission in Kisumu County, Kenya is | the intersection of socioeconomic vulnerabilities, including the 'sex for fish' (jaboya) economy along Lake Victoria. | a lack of education. In this essay I will discuss... [Autocompletes with generic essay template] |
In Topological Data Analysis (TDA), if an epidemiological dataset has a structural void representing an unobserved marginalized patient cluster, the corresponding persistent homology feature is represented by | a long-lived generator in the first homology group (H1) or higher. | a graph. Data analysis is the process of... [Fails to recognize algebraic topology concepts] |
To resolve memory exhaustion (OOM) when computing Betti numbers in an R Shiny app deployed on a Linux server, one should | increase the system RAM allocation or use efficient C++ implementations like 'TDAstats' or 'ripser' instead of native R loops. | use the memory.limit() function in R. [Hallucinates a Windows-only, deprecated function for Linux] |
When studying gender-based violence, using a naive machine learning model on hospital intake data will likely suffer from selection bias because | severe cases or cases resulting in death may never reach the hospital, leading to systemic underreporting of high-risk zones. | hospitals have small datasets. Therefore, big data is needed. [Misses the causal inference concept entirely] |
The primary assumption of the Kaplan-Meier estimator in survival analysis of HIV patients on ART is | that the censoring mechanism is non-informative and independent of the survival time. | that the data is normally distributed. [Classic statistical hallucination] |
The specific role of the National Cereals and Produce Board (NCPB) in Kenya's food security framework is | to procure, store, and maintain the strategic national food reserve to stabilize market prices and ensure food availability. | to plant crops for the government. [Hallucinates generic agricultural duties] |
In algebraic topology, the Cech complex of a point cloud is guaranteed to be homotopy equivalent to the union of the balls by the | Nerve Theorem. | Euler characteristic. [Confuses fundamental topological theorems] |
In mathematical epidemiology, the basic reproduction number (R0) is specifically defined as | the expected number of secondary cases produced by a single infection in a completely susceptible population. | the number of people who die from a disease. [Confuses transmission with mortality rate] |
When using the 'MatchIt' package in R for propensity score matching, setting 'replace = FALSE' means | each control unit can be matched to at most one treated unit (matching without replacement). | the data will not be overwritten in the memory. [Confuses statistical matching with computer memory allocation] |
When embedding causal graphs into Graph Neural Networks for predictive epidemiology, the mathematical purpose of the do-calculus operator is | to estimate the causal effect of an intervention by mathematically simulating the removal of incoming edges to the treated variable. | to do calculus faster on the GPU. [Fails to understand Pearl's causal frameworks] |
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Discovered Blind Spots
The model exhibited severe blind spots at the intersection of public health, localized African contexts, and advanced computational math. Specifically:
Hyper-Local Context: It hallucinates generic African stereotypes when asked about specific structural drivers of HIV in Kisumu, Kenya, completely missing documented socioeconomic factors.
Interdisciplinary Math: It fails to connect Topological Data Analysis (TDA) to epidemiological data structures, confusing persistent homology with generic graph theory.
Software Engineering: It confidently hallucinates deprecated, Windows-only R code (
memory.limit()) for Linux-based Shiny server deployments.Causal Nuance: It fails to understand causal selection bias in gender-based violence datasets, defaulting to generic "big data" platitudes.
Format Failure: As a base model, it frequently ignores the implicit QA nature of the prompt and simply autocompletes with generic essay structures ("In this essay I will discuss...").
Proposed Fine-Tuning Strategy
To fix these errors, the model requires Supervised Fine-Tuning (SFT) on a highly curated, domain-specific instruction dataset.
Dataset Composition: The dataset must include localized epidemiological reports (e.g., WHO and KEMRI data), advanced mathematical topology textbooks, and real-world R/Shiny deployment logs. Crucially, it must include "chain-of-thought" examples that force the model to differentiate correlation from causal inference.
Sourcing & Size: This dataset could be assembled by scraping MedRxiv preprints, GitHub repositories for spatial epidemiology, and African demographic health surveys (DHS). Because base models suffer from catastrophic forgetting if fine-tuned on too little data, I estimate a dataset of 15,000 to 25,000 high-quality instruction pairs would be required to successfully align the model into a specialized computational public health assistant without degrading its core language capabilities.
- Downloads last month
- 15