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)**

All reports were embedded using:

**Model:** `BAAI/bge-large-en-v1.5`
**Dimensionality:** 1024

Embeddings capture meaning (not keywords), allowing similar descriptions to cluster even with different phrasing, spelling, or vocabulary.

---

# **📉 2. Dimensionality Reduction (UMAP → 15D)**

High-dimensional vectors were reduced using:

**UMAP(n_components=15, metric='cosine')**

Reasons for UMAP-15:

* preserves local/global structure
* reduces noise
* improves cluster separation
* makes density-based clustering stable

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# **📍 3. Density Clustering (HDBSCAN)**

Reports were grouped using:

**HDBSCAN(min_cluster_size≈tuned, min_samples≈tuned)**

Outputs include:

* `cluster_id` (−1 = noise)
* `prob` (cluster stability score)
* ~3.7k clusters
* ~20% noise

HDBSCAN discovers meaningful themes like:

* recurring object behaviors
* atmospheric misidentifications
* military-adjacent patterns
* long-term witness motif clusters
* hoax/storytelling clusters
* nonsensical/noise clusters

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# **🔍 4. Sparse Retrieval (BM25) — Used for QA, Not in the Dataset**

A **BM25 index was built during preprocessing** to assist in quality control:

BM25 was used to:

* sanity-check embedding clusters
* inspect keyword cohesion
* identify outliers / mislabeled points
* verify that HDBSCAN clusters were semantically coherent
* detect keyword drift within large clusters

**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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# **🌕 5. Sidecar Feature Enrichment**

Each record includes enriched metadata:

### **Moon illumination & altitude**

* `moon_illum`
* `moon_alt_deg`
  Computed from timestamp + lat/lon.

### **Nearest airport (US/CA/GB accuracy strongest)**

* `nearest_airport_km`
* `nearest_airport_code`
  Computed via geospatial lookup.

### **Weather bucket**

* `wx_bucket` (high-level NOAA-based label, imperfect)

### **Timestamp normalization**

* `ts` = Unix epoch (ms)

### **Source tagging**

* `src` indicates which Kaggle dataset the row came from.

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# **📚 6. Canonical Output Format**

Each JSONL entry looks like:

```
{
  "uid": ...,
  "t_utc": ...,
  "lat": ...,
  "lon": ...,
  "text": ...,
  "src": ...,

  "city": ...,
  "state": ...,
  "country": ...,    

  "cluster_id": ...,
  "prob": ...,

  "moon_illum": ...,
  "moon_alt_deg": ...,

  "nearest_airport_km": ...,
  "nearest_airport_code": ...,

  "wx_bucket": ...,
  "ts": ...
}
```

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# **📌 What This Dataset *Is***

✔️ 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

---

# **📌 What This Dataset *Is Not***

❌ 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)