Nigeria Energy Access & Generation Mix Interpreter
Author: Hussein Adeiza (mabera)
Role: Licensed Environmental Health Officer, Abuja Nigeria
Base Model: Llama 3.3 70B
Fine-tuned with: AutoScientist by Adaption Labs
Model Description
A LoRA adapter fine-tuned to interpret Nigeria's electricity generation
and access data, producing structured analytical reasoning grounded
entirely in real, directly-downloaded statistics. Raw statistics go in,
expert quantitative interpretation comes out.
What Makes This Dataset Different
Unlike this author's other submissions, which cited real figures found
through research, every number in this dataset's training data was
pulled programmatically from a raw, unmodified source file, Our World
in Data's official energy dataset, downloaded directly from
github.com/owid/energy-data. No statistic was hand-typed. Every claim
in the training data is independently re-verifiable against the raw
9.2MB source file included in the dataset repository.
Training Data
- Source: Our World in Data, owid/energy-data (GitHub), downloaded
directly, 2025 release
- Dataset: 5 original prompt-completion pairs, every number computed
via pandas directly from the raw source file, expanded via Adaptive
Data (Hallucination Mitigation, full 20K+ datapoint expansion)
- Kaggle: https://www.kaggle.com/datasets/yunusahusseinadeiza/nigeria-energy-access-interpreter
Training Metrics — Strongest Result in This Portfolio
- Win rate (on dataset): 88% adapted vs 12% base model
- General Win Rate (Data-analysis-visualization domain): 81% adapted vs 19% base
- Base model: meta-llama/Llama-3.3-70B-Instruct
- Method: LoRA — Hallucination Mitigation, full 20,000+ datapoint expansion
- Dataset quality: 8.0 → 8.8 (+10.0% relative improvement, Grade B)
- Percentile: 31.5
- Domain classification: Data-analysis-visualization (60%) / Science (40%),
a clean, accurate match to the intended category
Why This Result Stands Out
This is the strongest win rate and General Win Rate in this author's
entire AutoScientist Challenge portfolio (15 submissions across Parts
1 and 2), exceeding even the closed-label AMR and Oil Spill submissions.
The likely contributing factors: genuinely verifiable ground-truth data
sourced directly from a raw file rather than research synthesis, a
clean domain match routing to the strong base model, and the full
datapoint expansion (unlike the closed-label submissions, this dataset
carries no risk of label-taxonomy drift, so the fuller expansion could
be used without the precision concerns that applied elsewhere).
Key Cited Findings (from the raw downloaded source only)
- Nigeria's per-capita electricity access (174.9 kWh/year) sits at
roughly 1/22nd of the world average (3,859.8 kWh) and South Africa's
level (3,749.3 kWh), despite Nigeria's far larger population (237.5
million vs South Africa's 64.7 million)
- Nigeria's generation mix shifted meaningfully in a single year:
gas share fell from 75.0% (2024) to 68.7% (2025), while hydro rose
from 24.5% to 30.9%
- Over the full 25-year period (2000-2025), the long-term trend remains
toward increasing gas dependency (+6.9 percentage points), despite
the 2025 single-year uptick in hydro
- Nigeria's solar share of generation (0.313%) sits at roughly 3.6% of
the world average (8.745%), despite Nigeria's equatorial location
giving it substantially higher solar potential than most solar-
adopting nations
Credits
Powered by Adaptive Data — Adaption Labs
AutoScientist Challenge 2026, Part 2 — Data Visualization Category
