Adesso, vediamo i dettagli sullo score. Ho fatto leggere l'intero documento a GPT-5, e queste sono el conclusioni, sembra che lo score venga applicato solo al range di periodo di interesse (definito di default da rexelweb o modificato da noi).
Got it — in the ITACA REXELweb context, the *“score”* field you see alongside each waveform (and the *mean_score* field for a whole set) is a **numeric quality measure of how well the selected record’s spectrum matches the chosen target spectrum** within the period range and tolerance you set.
Here’s the meaning in detail:
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## 🔍 What the “score” is
- Mathematically, it’s the **root‑mean‑squared error (RMSE)** between two curves:
1. The spectral ordinates of the record’s (or the set’s) **mean response spectrum**.
2. The ordinates of the **target design spectrum** you defined in step 1.
- The comparison is made **only over the periods in your match window** (T₁–T₂) and using the units in the plot (usually cm/s² for PSA).
- Lower is better:
- **0.000** = a perfect overlay in that period range.
- Values like 0.05–0.10 mean the average spectrum deviates by about 5–10 % RMS from the target.
- Larger values mean a looser fit.
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## 📄 Where it appears
In the JSON/metadata for each waveform or set, the manual defines:
- `"score"` → RMSE for **each** component in the combination.
- `"mean_score"` → RMSE averaged **across all** components in the set (for quick comparison of different suites).
- `"mean_spectrum_score"` → the RMSE curve values at the specific match periods (the period‑by‑period deviation, not averaged).
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## 🧮 How to interpret it
- **Compare within the same match settings** — you can rank candidate records or sets by lowest score.
- Use it **with scale factor info**: a low score with SF≈1 means the unscaled record already fits well; low score with large SF means good fit but heavy amplitude adjustment.
- In 3‑component runs, the manual also reports:
- `"vertical_mean_spectrum_score"` for vertical match quality.
- `"vertical_target_spectrum_score"` for the target vertical.
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If you want, I can sketch **the exact RMSE formula** they’re using and a tiny numerical example with fake spectra, so you can see how a 0.09 “score” actually comes out of the point‑by‑point differences. Would that help you lock it in?
