| This dataset is not raw UFO reports — it’s a *processed, enriched, semantically-clustered corpus* designed for large-scale analysis. |
| Below is the exact pipeline used. |
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| # **🧠 1. Embeddings (Semantic Encoding)** |
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| All reports were embedded using: |
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| **Model:** `BAAI/bge-large-en-v1.5` |
| **Dimensionality:** 1024 |
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| Embeddings capture meaning (not keywords), allowing similar descriptions to cluster even with different phrasing, spelling, or vocabulary. |
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| # **📉 2. Dimensionality Reduction (UMAP → 15D)** |
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| High-dimensional vectors were reduced using: |
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| **UMAP(n_components=15, metric='cosine')** |
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| Reasons for UMAP-15: |
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| * preserves local/global structure |
| * reduces noise |
| * improves cluster separation |
| * makes density-based clustering stable |
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| --- |
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| # **📍 3. Density Clustering (HDBSCAN)** |
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| Reports were grouped using: |
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| **HDBSCAN(min_cluster_size≈tuned, min_samples≈tuned)** |
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| Outputs include: |
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| * `cluster_id` (−1 = noise) |
| * `prob` (cluster stability score) |
| * ~3.7k clusters |
| * ~20% noise |
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| HDBSCAN discovers meaningful themes like: |
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| * recurring object behaviors |
| * atmospheric misidentifications |
| * military-adjacent patterns |
| * long-term witness motif clusters |
| * hoax/storytelling clusters |
| * nonsensical/noise clusters |
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| --- |
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| # **🔍 4. Sparse Retrieval (BM25) — Used for QA, Not in the Dataset** |
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| A **BM25 index was built during preprocessing** to assist in quality control: |
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| BM25 was used to: |
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| * sanity-check embedding clusters |
| * inspect keyword cohesion |
| * identify outliers / mislabeled points |
| * verify that HDBSCAN clusters were semantically coherent |
| * detect keyword drift within large clusters |
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| **Important:** |
| The *BM25 scores and index are **not included** in the final dataset.* |
| BM25 influenced the cleaning stage but is not part of the exported fields. |
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| --- |
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| # **🌕 5. Sidecar Feature Enrichment** |
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| Each record includes enriched metadata: |
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| ### **Moon illumination & altitude** |
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| * `moon_illum` |
| * `moon_alt_deg` |
| Computed from timestamp + lat/lon. |
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| ### **Nearest airport (US/CA/GB accuracy strongest)** |
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| * `nearest_airport_km` |
| * `nearest_airport_code` |
| Computed via geospatial lookup. |
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| ### **Weather bucket** |
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| * `wx_bucket` (high-level NOAA-based label, imperfect) |
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| ### **Timestamp normalization** |
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| * `ts` = Unix epoch (ms) |
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| ### **Source tagging** |
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| * `src` indicates which Kaggle dataset the row came from. |
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| --- |
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| # **📚 6. Canonical Output Format** |
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| Each JSONL entry looks like: |
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| ``` |
| { |
| "uid": ..., |
| "t_utc": ..., |
| "lat": ..., |
| "lon": ..., |
| "text": ..., |
| "src": ..., |
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| "city": ..., |
| "state": ..., |
| "country": ..., |
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| "cluster_id": ..., |
| "prob": ..., |
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| "moon_illum": ..., |
| "moon_alt_deg": ..., |
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| "nearest_airport_km": ..., |
| "nearest_airport_code": ..., |
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| "wx_bucket": ..., |
| "ts": ... |
| } |
| ``` |
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| --- |
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| # **📌 What This Dataset *Is*** |
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| ✔️ A semantically-clustered UFO corpus |
| ✔️ Enriched with astronomy + geospatial + weather sidecars |
| ✔️ Cleaned, deduped, normalized |
| ✔️ Built using modern ML (BGE+UMAP+HDBSCAN) |
| ✔️ Ready for search, visualization, mapping, temporal analysis |
| ✔️ Distributed without interpretation or claims |
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| --- |
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| # **📌 What This Dataset *Is Not*** |
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| ❌ Not a curated list of “important” sightings |
| ❌ Not opinionated — no inferences built in |
| ❌ Not a proof of anything |
| ❌ Not filtered toward any outcome |
| ❌ Does not include BM25 scores (BM25 was QA-only) |
