Add an AI slop verdict that downrates the author's future articles

A fourth explicit verdict, "AI slop" (Vote::Slop, label `slop`), joins the
rating widget on the web, the EPUB footer links, the /r/ confirmation page,
the CLI, imports and the dashboard filters. It counts as a full negative
(curation.feedback.slop_value, -1.0) in the neighbour and affinity signals.

Beyond that, each run loads the authors whose current verdict is slop, with
no lookback, and multiplies the preliminary blend and the utility of every
candidate by that author by 1 - curation.ranking.slop_author_penalty (0.75),
so they sink before triage. The flag is recorded in signals_json, shown by
`explain` and the dashboard signals table, and the confirmation names the
author (or says no author is known, in which case only the rating applies).

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01VXYGBPoHZSDSfE5WcJ9bvj

README.md

@@ -15,7 +15,8 @@ publishes the lot over its own OPDS catalog — which doubles as a
 [BookOrbit](https://github.com/thallada/bookorbit) watched folder if you run one.
 Each article chapter ends with a one-line note of what the pipeline made of the
 piece (topic, format, depth, and the reader interests it matched) and Loved it /
-Good / Not for me links that feed back into tomorrow's curation; a short *Behind
+Good / Not for me / AI slop links that feed back into tomorrow's curation (an AI
+slop report also cuts every future article by that author); a short *Behind
 the paper* chapter before the colophon says what the run considered, how the
 deep set was admitted, whether the learned signals were active, the ten
 highest-utility near misses, and what it all cost.
@@ -125,7 +126,7 @@ is current.
 daily-epub generate [--date YYYY-MM-DD] [--dry-run] [--out DIR] [--max-articles N] [--skip-llm] [--skip-embeddings] [--rescore]
 daily-epub serve                # public site, private dashboard, OPDS, ratings, downloads
 daily-epub profile rebuild      # regenerate learned profile adjustments
-daily-epub ratings list [--days 90] [--label loved|good|down|cleared]
+daily-epub ratings list [--days 90] [--label loved|good|down|slop|cleared]
 daily-epub ratings set --article 42 --label loved --note "excellent"
 daily-epub ratings clear --url https://example.com/article
 daily-epub explain --date YYYY-MM-DD (--article ID | --url URL) [--run-id N]
@@ -403,6 +404,7 @@ prints what resolved.
 | `curation.feedback.loved_value` | `1.0` | Weight for a Loved it verdict. |
 | `curation.feedback.good_value` | `0.35` | Weight for a Good verdict. |
 | `curation.feedback.not_for_me_value` | `-1.0` | Weight for a Not for me verdict. |
+| `curation.feedback.slop_value` | `-1.0` | Weight for an AI slop verdict; the author penalty is `curation.ranking.slop_author_penalty`. |
 | `curation.feedback.verdicts_in_prompt` | `60` | Recent explicit verdicts included in the system prompt. |
 | `curation.recent_rejection_days` | `7` | Churn window for recent low triage/deep assessments. |
 | `curation.recent_rejection_floor` | `3.0` | Scores below this floor are excluded during the churn window (except auto-includes). |
@@ -448,7 +450,10 @@ gated: `knn` (rated-neighbour preference) ramps from `knn_floor` (8) to
 `feed_floor` (15) to `feed_full` (40) attributable ratings; below the floor the
 signal is absent. Ratings decay with `rating_half_life_days` (60) over
 `rating_lookback_days` (180); `neighbour_k` (5) neighbours per side and
-`negative_coefficient` (0.75) shape the signal. `triage_max` (800),
+`negative_coefficient` (0.75) shape the signal. `slop_author_penalty` (0.75)
+is the fraction of the blend and utility removed from every candidate whose
+author currently carries an AI slop verdict, on any feed and with no age
+limit. `triage_max` (800),
 `deep_keep` (120), `shortlist_keep` (60), `assessment_reuse_days` (3),
 `semantic_min_words` (300), `exploration_slots` (5), `[curation.ranking.quotas]`
 (`triage` 60 · `interest` 20 · `knn` 20), `[curation.ranking.weights.utility]`
@@ -854,7 +859,7 @@ curl -s https://daily.hallada.net/healthz
 curl -s https://daily.hallada.net/opds/daily.xml | head
 curl -s https://daily.hallada.net/issues.json | jq '.[0]'
 
-# 7. Feedback loop: tap Loved it / Good / Not for me in KOReader, then
+# 7. Feedback loop: tap Loved it / Good / Not for me / AI slop in KOReader, then
 sqlite3 /var/lib/daily-epub/daily-epub.db 'select * from rating_events order by event_at desc;'
 
 # 8. Watch cost and quality for a week