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README.md

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+# ecocoder-cot-v1 — Ecological Chain-of-Thought Dataset
+
+**10 CoT traces** for fine-tuning Nemotron on ecological reasoning + code generation.
+
+## Format
+
+Each trace has 3 sections:
+
+```
+[CONTEXT] {paper abstract + method description}
+[REASONING] {step-by-step ecological reasoning}
+[CODE] {Python/R implementation}
+```
+
+## Splits
+
+| Split | Traces | Size |
+|-------|--------|------|
+| train | 8 | ~40 KB |
+| test  | 2 | ~10 KB |
+
+## Papers Covered
+
+| # | Paper | Method | Code |
+|---|-------|--------|------|
+| 1 | GLOSSA (2505.05862) | BART Bayesian SDM | R |
+| 2 | MaskSDM (2503.13057) | DL + Shapley values | PyTorch |
+| 3 | GeoThinneR (2505.07867) | kd-tree thinning | R |
+| 4 | HeteroGNN (2503.11900) | Graph Neural Net | PyTorch Geometric |
+| 5 | CISO (2508.06704) | Conditional SDM | PyTorch |
+| 6 | BioAnalyst (2507.09080) | Foundation Model | PyTorch |
+| 7 | MultiScale (2411.04016) | Multi-scale SDM | PyTorch |
+| 8 | LD-SDM (2312.08334) | LLM + Taxonomy | PyTorch + HF |
+| 9 | PointProcess (2311.06755) | Poisson Process | R/INLA |
+| 10 | EntropyBias (2508.02272) | Shannon Entropy | Python + R |
+
+## Intended Use
+
+Fine-tune `nemotron-3-nano-30b-a3b` (32.5B) with Unsloth 4-bit QLoRA on A100 80GB.
+
+### Training config
+
+```python
+from unsloth import FastLanguageModel
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name="nvidia/Nemotron-3-Nano-30B-A3B-ablated",
+    max_seq_length=4096,
+    load_in_4bit=True,
+)
+```
+
+## Generation Pipeline
+
+```
+Papers (arXiv) → DeepSeek v4 Pro CoT → JSONL → HuggingFace Dataset → Unsloth QLoRA → ecocoder-nemotron
+```
+
+## Next: v2 (100 traces)
+
+Scale to 100 papers across 6 SDM categories: Bayesian methods, deep learning, spatial methods, taxonomic integration, data integration, bias correction.
+
+---
+
+Built with DeepSeek v4 Pro · ecoseek-litdump · alrobles

dataset_dict.json

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+{"splits": ["train", "test"]}

test/data-00000-of-00001.arrow

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test/dataset_info.json

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+{
+  "citation": "",
+  "description": "",
+  "features": {
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+      "_type": "Value"
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+    "paper_title": {
+      "dtype": "string",
+      "_type": "Value"
+    },
+    "text": {
+      "dtype": "string",
+      "_type": "Value"
+    }
+  },
+  "homepage": "",
+  "license": ""
+}

test/state.json

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+}

train/data-00000-of-00001.arrow

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train/dataset_info.json

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+{
+  "citation": "",
+  "description": "",
+  "features": {
+    "paper_arxiv_id": {
+      "dtype": "string",
+      "_type": "Value"
+    },
+    "paper_title": {
+      "dtype": "string",
+      "_type": "Value"
+    },
+    "text": {
+      "dtype": "string",
+      "_type": "Value"
+    }
+  },
+  "homepage": "",
+  "license": ""
+}

train/state.json

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+    }
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+}