fix(document-processors): correct cascade escalation metric + review nits

Address round-1 review on PR #914:
- Attribute the OCR hop in a fast->structured->ocr inline cascade to
  from_tier="structured" (not a second "fast" escalation), so
  astrolabe_document_escalation_total per-tier counts stay accurate.
- Add test_inline_fast_structured_ocr_cascade pinning that two-hop path and the
  metric attribution.
- Note in classify_from_text that its doc-level control ratio is over full_text
  (all pages), not the sampled subset classify_pdf uses.
- Clarify that corrupt_glyphs never lands in the suppressed-escalation counter.
- Dedupe the glyph-corrupt test string into tests/fixtures/glyph_corruption.py.
- Use pytest.approx for the control-char-ratio zero checks (SonarCloud S1244).

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

nextcloud_mcp_server/document_processors/classifier.py

@@ -407,9 +407,12 @@ def classify_from_text(
     # page_count guard also skips escalation; defaulting to 0.0 keeps the
     # recorded classification metric accurate rather than a misleading "ocr").
     ocr_frac = (sum(p.needs_ocr for p in pages) / sampled) if sampled else 0.0
-    # Doc-level (char-weighted) control-char ratio -- the glyph-leak signal that
-    # routes to the structured tier. Computed over full_text so it is robust to
-    # boundary edge cases.
+    # Doc-level control-char ratio -- the glyph-leak signal that routes to the
+    # structured tier. Computed over the WHOLE full_text (all pages), unlike
+    # classify_pdf which char-weights the up-to-MAX_SAMPLED_PAGES sample; the two
+    # are therefore not numerically identical for a >24-page doc with corruption
+    # concentrated outside the sample. full_text is used here because it is exactly
+    # the text that gets chunked + indexed and is robust to boundary edge cases.
     control_ratio = _control_char_ratio(full_text)
 
     flags, recommended = _route_from_signals(