Aggregated reader reviews are widely used as scalable proxies for book attributes in computational literary studies, recommendation, and narratological-benchmark design, but the field lacks a per-attribute audit of what review aggregates can reliably recover. We audit eight review-derived feature methods on 391 attributes, using the HumanReader survey corpus (Knight et al., 2026) as ground truth. Most apparent predictive performance is popularity-confounded. Only 34.9% of evaluable attributes have a method that adds signal beyond a popularity proxy (popularity-conditioned incremental ΔR² > 0.01), and just 12 additionally pass held-out validation, multiplicity correction, and a stricter ΔR² > 0.05 release gate. Recoverability is significantly associated with an attribute’s "lexical anchorability," i.e. how often reviews mention keywords in the attribute’s semantic neighborhood, with high-anchorability attributes about 2.3 times more likely to clear validation (50% of the top anchorability quartile, 22% of the bottom). We release HumanReader-Reviews-1.7M, per-book predictions on 12 content-driven attributes (Tier A) and 77 validated but popularity-informed attributes (Tier B), with per-method test statistics on the 111 attributes no scalable method recovers (Tier C). Raw held-out R² is a liberal estimate of per-book content recovery, crediting any popularity-correlated content, while popularity-conditioned ΔR² is conservative in what it attributes to content; we report both. On a coverage-limited probe of 31 attributes, direct LLM-on-reviews annotation recovers signal the release-panel methods miss, but at a compute cost prohibiting per-book deployment across the 1.7M-book corpus; as a complement, we release an open-weight Qwen 2.5-32B companion scoring 9,990 books.