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).