| 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. | |
| --- | |
| # **🧠 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 | |
| --- | |
| # **📍 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 | |
| --- | |
| # **🔍 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. | |
| --- | |
| # **🌕 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. | |
| --- | |
| # **📚 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": ... | |
| } | |
| ``` | |
| --- | |
| # **📌 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) |