---
license: apache-2.0
language:
- en
- ha
- yo
tags:
- environment
- nigeria
- oil-spill
- niger-delta
- pollution
- africa
- autoscientist
base_model: meta-llama/Llama-3.3-70B-Instruct
---

# Nigeria Oil Spill 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
A LoRA adapter fine-tuned to interpret raw oil spill statistics from
Nigeria's NOSDRA and NUPRC regulatory data and produce structured
environmental analytical reasoning, not question-and-answer pairs.
Raw statistics go in, expert interpretation comes out, grounded
strictly in cited source data.

## Training Data
- Source: NOSDRA via TheCable and Nairametrics, NUPRC via Platform
  Times and TheCable, Frontiers in Environmental Science, FairPlanet,
  Pulitzer Center, ScienceDirect, climatechangenews.com
- Dataset: 5 original cited prompt-completion pairs, expanded via
  Adaptive Data (House Special + Hallucination mitigation only,
  Reasoning Traces deliberately excluded)
- Languages: English, Hausa, Yoruba
- Quality improvement: 18.8% relative (Grade B → A, score 8.0 → 9.5)
- Kaggle: https://www.kaggle.com/datasets/yunusahusseinadeiza/nigeria-oil-spill-environmental-interpreter
- Hugging Face dataset: https://huggingface.co/datasets/mabera/nigeria-oil-spill-dataset

## Training Metrics
- Win rate (on dataset): 73% adapted vs 27% base model
- **General Win Rate (unseen Science-domain tasks): 79% adapted vs 21% base**
- Base model: meta-llama/Llama-3.3-70B-Instruct
- Method: LoRA — House Special + Hallucination mitigation
- Dataset quality: 8.0 → 9.5 (+18.8% relative improvement, Grade A)

## Why the General Win Rate Result Matters
At 79% on unseen Science-domain tasks, this is the highest General
Win Rate in this portfolio, beating even the Desertification model's
77%. The model scored higher on unseen tasks than on its own training
distribution (79% vs 73%), confirming the same healthy generalization
pattern seen on the previous submission and suggesting these adapters
are not narrowly overfitting on their training data.

## Recipe Note
Reasoning Traces were deliberately excluded from this submission
following a finding on a prior submission (Nigeria Desertification
Analysis Model) where expanded training rows included elaboration
not traceable to the original cited sources. House Special +
Hallucination mitigation alone produced Grade A results with the
highest General Win Rate in this portfolio, confirming this was
the right call.

## Key Cited Findings (from original source data only)
- Nigeria's oil spill incident count and spilled volume have decoupled
  since 2021: 2023 had roughly 3x the incidents of 2021 but a lower
  spilled volume, meaning neither metric alone tells the full story
- Two government agencies (NOSDRA and NUPRC) reported a 24%
  discrepancy in incident counts for the same year (589 vs 732 in
  2024), consistent with a documented historical pattern of 300 to
  3,000 occurrences divergence between agencies and operators
- No legally binding penalties or fines currently exist for oil spills
  in Nigeria
- Ogoniland remediation stands at approximately 11% completion 13
  years into a 25 to 30 year UN-mandated timeline, with 2024 field
  assessments finding contamination 260x above regulatory standards
  at officially certified remediated sites

## Credits
Powered by Adaptive Data — Adaption Labs
AutoScientist Challenge 2026