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.
Improves on the classic (kudos,hits) metric. This score combines 3 metric pairs correlated with some of my favorite fics (check graphs at the bottom): (bookmarks,kudos), (collections,kudos) and (comments,kudos).
Metrics can be disabled individually, for example, set THRESHOLDS { 'comments': bmin=Infinity, min=Infinity } to disable the contribution of the less accurate (comments,kudos) metric.
Setting the 'kudos' thresholds will affect all 3 metric pairs since they share it, you can for example raise its dim_below to hide more small fics.
(All default thresholds are tuned at the lowest possible to filter noise from data, lowering them may un-dim inaccurate scores.)
(If editing the code, make sure to disable auto-updates to preserve your changes.)
(You can observe the 3 scores by setting const DEBUG = true;.)
Autosort: Navbar toggle ⇊|⇅ (sorted|default).
Indicator position: Navbar toggle ⇱|⇲ (top|bottom).
The ⇱ now dodges the year to stands out, and to stays visible on fics collapsed by KH or KHX .
Dimming: Applied to low-confidence scores; dimmed fics are sorted at the end.
Toggles do not need reloading. Colors have automatic dark mode.
v2.26: - Improved scoring for extreme values (Distributions are centered at 0 in case someone lowers bmin. Extreme highs are denoised and future proofed using parallel lines past the cutoff.)
v2.23: - Added separate ALPHA for dimmed works, and updated defaults.
v2.22:
- Enhanced DEBUG indicators.
- I realised that (comments,kudos) has two different distributions if chapters==1 or chapters>=2, probably because people interact with oneshots differently. Adding a min threshold to disable (comments,kudos) if chapters<2 fixed its distribution. I have not tried to model this metric separately for chapters==1.
- I rewrote the algorithm to use stronger minimums, with a dimmed fallback to the old ones.
v2.21:
- Added tunable z_scale to produce fewer scores from one metric should you prefer one.
- Option to dim/fail if fewer than N metrics pass, should make alpha<1 more interesting for those who use it. Requiring multiple metrics gives more values to mix making the Average more meaningful (Average did nothing when 1 metric passed. Now, it will reward scores with all-N high metrics).
v2.20:
- Looking closely at the graphs, Kudos should have been trained on min=8, this cleaned up the distributions.
- Default is back to Max, the rounding off affected large works only. Small works get more 100s because 1/3 metric is activated.
Tested: tCDF makes fewer 100s, spreads middle 1pt both sides, but 6x expensive; tCDF_norm does opposite; sticking with CDF.
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.
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:
• 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.
• 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:
Update: I also tested (kudos,hits/chapters) and it is strongly bimodal (1 chapter, 2+ chapters) and just as random as (kudos,hits). In fact I suspect that all metric pairs are bimodal and that 1-chapter works should ideally be modeled separately.
