GFork

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Indicator position: Toggle ⇱|⇲ (top|bottom), ⇱ dodges the year in corner (outdated pic); stands out more and stays visible on fics collapsed by KH or KHX . 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 small 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, and α*Max+(1-α)*Average for the final score.

Below: Max, Average, and Final blended score, with α=0.95 and α=0.68.

Demonstrates how lowering α mixes more Average and shifts the scores left (including the red dots -- my favorite fics). Therefore, Max is the better metric, but i set the default at α=0.95 to round it a bit and have fewer 100 score (Default is back to full Max).

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.