final_audit.md

# Phase 5, Step 5.4 — Final Quality Spot-Check

Samples drawn from the ASSEMBLED FINAL corpora (`data/final/{category}/final.jsonl`),
seed=42, via `scripts/assemble/sample_final_audit.py` — distinct from Step 4.4's
audit, which sampled from the pre-assembly `filtered.jsonl` files. This is the
last line of defense before delivery.

## Math (200-record sample, seed=42, from `data/final/math/final.jsonl`)

Read 40 of 200 sampled records in full detail.

### Ratings
- ✅ Good: 29/40 (72.5%) — genuine, on-target K-12 through advanced math and
  applied-STEM content: worked physics/EE/thermodynamics problems, forum
  Q&A (PhysicsForums, MathHelpForum, MathOverflow, StackExchange), exam
  content (CBSE, board exams, GMAT/GRE quant), logic puzzles, real analysis
  and number theory, MATLAB/R applied-math code, K-12 arithmetic word
  problems. Several records use a listicle/FAQ blog format but contain
  factually correct, pedagogically useful content (e.g. "What is the
  difference between real numbers and integers?", "How to calculate 2/3 of a
  number") — style alone was not treated as disqualifying.
- ⚠️ Borderline: 5/40 (12.5%) — legitimate but low-value: a bare index page
  of NRICH problem titles/links with no actual problem content, a
  commercial worksheet product page with a genuine explanatory tail, a
  CourseHero paywall preview truncated mid-content, a WikiAnswers page with
  messy comment-thread residue attached to an otherwise-fine answer.
- ❌ Bad: 6/40 (15%) — **above pipeline.md's <10% threshold.** Specific
  findings, each a distinct pattern:
  1. **AI-generated clickbait filler** (2 instances, e.g. "Mind-Blowing
     Result: You Won't Believe What Happens When You Divide 70 By 5!") —
     sensationalized SEO content with no real mathematical depth. Already
     documented as a known residual limitation in `quality_audit.md` (Step
     4.4 found the same pattern, sample #25).
  2. **Off-topic forum comment-thread dump** — a robot-automation/UBI/
     politics discussion thread (source: i-am-bored.com) containing a couple
     of incidental dollar-figure calculations, which was enough to pass
     finemath4plus's classifier-trust shortcut (density/language checks are
     skipped for this source per the Step 4.1 AMC-8 fix) despite having
     almost no real educational math content. A new pattern — similar in
     spirit to the Reddit-dump issue already fixed, but from a different
     domain/format with no shared detectable signature.
  3. **Content-aggregator dump** — a Q&A-farm page splicing together several
     unrelated questions under an irrelevant title, including one entirely
     off-topic, non-English question (Tagalog, about a typhoon's landfall).
  4. **Thin SEO template page** — a "NCERT Solutions for Class X Maths"
     listing that only describes chapter topics in generic boilerplate,
     repeated near-verbatim across chapter headers, with no actual worked
     content.
  5. **Scraping artifact** — a factoring-trinomials page with unrelated
     sidebar/related-links text (from completely different topics — pop
     culture quotes, gaming guides, unrelated products) spliced into the
     main text field, a scraper capturing page chrome alongside content.
  6. **Essay-mill / homework-cheating-service advertisement** — a legitimate
     econometrics/regression homework problem wrapped in explicit marketing
     copy for a paid paper-writing service ("Zero-plagiarism guarantee",
     "Money-back guarantee", "Free-revision policy"). The most concerning
     individual finding: this doesn't just lower quality, it embeds
     promotional content for an academic-dishonesty service inside an
     educational corpus.

### Conclusion and decision
This sample's bad rate (15%, n=40) exceeds pipeline.md's 10% threshold and is
higher than the earlier Step 4.4 pre-assembly audit (7.5%, n=40) — a real
signal, not just sampling noise, though also not enormous in absolute terms.
All six bad findings are DIFFERENT patterns from each other and from the
already-fixed Reddit-dump issue; none has a cheap, low-false-positive
detection signature the way the Reddit vote-marker did. Flagged to the user
with the tradeoff (accept as a documented limitation vs. build one more
targeted fix vs. broadly re-tighten finemath4plus's skipped density/language
checks, at the cost of reintroducing the AMC-8 false-positive problem that
motivated skipping them in the first place). **User decision: accept and
proceed to delivery** — the corpus is still ~85% good+borderline (72.5%
cleanly good), token targets are comfortably met (1.005B, 0.5% over target),
and further tightening risks re-losing the legitimate terse/notation-heavy
content the AMC-8 fix was specifically designed to recover. Documented here
as a known, accepted residual limitation rather than silently ignored.

## Code (200-record sample, seed=42, from `data/final/code/final.jsonl`)

Read 46 of 200 sampled records in full detail, spanning all 9 sources
(githubcode x5 across python/javascript/java/html/css, freecodecamp,
exercism, apps, codecontests).

### Ratings
- ✅ Good: 43/46 (93.5%) — real, functional, well-formed code across all
  languages and sources: production libraries (urllib3, IntelliJ, Quarkus,
  Apache Aries), application code (Django/Angular/Android), freeCodeCamp
  lesson content with solutions, Exercism canonical solutions, and
  competitive-programming problems with correct multi-language solutions
  (Codeforces via CodeContests).
- ⚠️ Borderline: 2/46 (4.3%) — auto-generated artifacts that are real but
  not educational: a Clover test-coverage-instrumentation JS file, a
  Doxygen HTML search-index JS data file. Consistent with the same
  "auto-generated docs slip through" gap already documented in
  `quality_audit.md`'s code section (there found for Sphinx; here for
  Clover/Doxygen — same category of gap, different tools).
- ❌ Bad: 1/46 (2.2%) — a near-empty JS build-artifact stub containing only
  `"use strict"` boilerplate and a base64-encoded sourcemap comment, with
  zero actual logic. A transpiler/bundler output file that neither the
  `is_minified` nor `is_autogenerated` heuristics were designed to catch
  (it's not minified — it's just empty — and carries no "auto-generated"
  marker text).

### Conclusion
Both thresholds (<10% bad, <30% borderline) are comfortably met — consistent
with Step 4.4's pre-assembly code audit (~90% good, ~10% borderline, 0% bad).
No further action needed for code; the two minor gaps found are the same
class of low-frequency, low-severity issue already accepted for the code
category.

## Overall Step 5.4 conclusion
Code passes cleanly. Math has a real, above-threshold bad rate (15%) driven
by diffuse, low-frequency patterns with no cheap fix — flagged to the user,
who chose to accept it as a documented residual limitation given the corpus
is still ~85% good+borderline and both categories comfortably exceed their
1B-token targets. Proceeding to Phase 6 (Documentation & Delivery).