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Autosorting 'Quality' Indicator trained on 11k+ works. Very generous with small fics, rewards engagement over popularity (bookmarks-collections-comments/kudos instead of hits) with a 0-100 score spread. Sort & position toggles included.
当前为

Autosort: Toggle ⇊|⇅ (sorted|default).
Indicator position: Toggle ⇱|⇲ (top|bottom), ⇱ dodges the year in corner (outdated pic); stands out more and stays visible on fics collapsed by @Min_'s Kudosed-history. Toggles do not need reloading. Colors have automatic dark mode.
Dimming: Applied to low-confidence, can modify dim_below atop the code to hide more fics (disable updates to keep your changes). Dimmed fics are sorted at the end.
v2.19:
- Updated the formula and fixed the defaults. Now using a Normal regression per-metric with a mix of 95% Max and 5% Average for the final score. 

v2.13:
- Option to take the AVERAGE of all metrics instead of their MAX in the code. (the scale becomes a blending weight instead of a cap in that case). Gives a nicer spread while still blending.
v2.12:
- The (comments/kudos) metric is back among (bookmarks/kudos) and (collections/kudos).
- Uniformization is disabled by default atop the code: it was illogical considering the use of max() to surface the best metric. This means you will see more high scores.
- Can disable a metric, scale it down, or raise its min contribution floor in the code. 

v2.3: Switched to GAM score {Bk,Col/Ku} instead of Polynomial score {Bk,Col,Com/Ku}. (can install v2.7 to compare both with the Q/P toggle)
v2.0:
• Log() Engagement metrics (bookmarks/collections/comments/kudos) selected due to high correlation with my top favorite fics. By comparison the classic (x/hits) metrics feel completely random:
• 2nd-degree polynomial quantile regressions (P10, P50, and P90) of the 3 selected metrics pairs:
For each pair, an individual score is computed by:
◦ Deriving a skewed normal distribution from the polynomial contours.
◦ Z-score calculation based on its deviation from the P50 center-line.
◦ Converted into a 0-100 inverted percentile rank via NCDF.
• Final score == Max of all 3 reliable scores, gated by minimum stat counts.
The score is mapped to a perfect 0-100 percentile rank via ECDF normalization for uniform scoring.