Nigeria Desertification Analysis Model
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
This is a LoRA adapter fine-tuned to interpret raw desertification
statistics from Nigeria's 11 frontline Sahel-bordering states and produce
structured environmental analytical reasoning, grounded in peer-reviewed
remote sensing research and UNCCD/UNEP figures.
⚠️ Known Limitation — Please Read Before Use
During Adaptive Data expansion (House Special + Reasoning Traces), the
generated training rows included elaboration beyond what the 5 original
cited source rows actually support, specific tree species, a numeric
"forest half-life" estimate, and an albedo feedback-loop explanation that
do not trace to any cited source. This was caught at the recipe-review
stage but the dataset was adapted with Reasoning Traces enabled anyway.
Treat any specific figure or mechanism from this model that is not
also present in the original 5-row source dataset as unverified model
elaboration, not a cited fact. The original key-findings reference
table (linked below) reflects only genuinely cited statistics.
Update: raised this directly with the Adaption team at the June 25
Research Hour. They confirmed this is a known gap and that stricter
source-grounding for Reasoning Traces expansion is being addressed
on their end.
Training Data
- Source: UNCCD Nigeria Country Profile, UNEP, Sambe et al. 2026,
Ibrahim et al. 2022
- Dataset: 5 original cited prompt-completion pairs, expanded via
Adaptive Data (see limitation above)
- Languages: English, Hausa, Yoruba
- Quality improvement: 31.4% (Grade B → A)
- Kaggle: https://www.kaggle.com/datasets/yunusahusseinadeiza/nigeria-desertification-environmental-interpreter
- Hugging Face dataset: https://huggingface.co/datasets/mabera/nigeria-desertification-dataset
Training Metrics
- Win rate (on dataset): 73% adapted vs 27% base model
- General Win Rate (unseen Science-domain tasks): 77% adapted vs 23% base
- Base model: meta-llama/Llama-3.3-70B-Instruct
- Method: LoRA — House Special + Reasoning Traces + Hallucination mitigation
- Dataset quality: 7.0 → 9.2 (+31.4% improvement, Grade A)
Why the General Win Rate Result Matters
This is the first submission in my portfolio evaluated against Adaption's
new global held-out test set rather than only the training-specific
metric. The model scored higher on unseen Science-domain tasks (77%)
than on its own training distribution (73%), a positive signal against
overfitting on this small 5-row source dataset.
Key Cited Findings (from original source data only)
- Nigeria's frontline states saw 14x more deforestation than successful
afforestation over a 25-year remote sensing study
- Desertification continued expanding even in years with more favorable
rainfall and temperature, suggesting drivers are substantially
decoupled from climate variability
- Overgrazing accounts for ~58% of land degradation in the region
Credits
Powered by Adaptive Data — Adaption Labs
AutoScientist Challenge 2026
