hit@8 = 5 of 6, every hit at rank 1, against a chance baseline of 1.35 of 6
over a denominator of 629 concepts per question. Wall time 0.51-0.56 s per
question; spent 17 970 - 74 838 bytes against a 120 000 limit.
Two things this measurement did NOT establish, both in the report:
- BOTH known-negative controls FAILED. A question the bundle has no answer to
still returns eight excerpts, because no natural Norwegian question is
lexically disjoint from a 629-concept corpus under a four-character
shared-prefix rule -- measured per token, the interrogative `hvor` reaches 40
concepts, `brukes` 83. So `no_lexical_match` works per concept and not as a
whole-question gate: an empty excerpt list is evidence of absence, a full one
is not evidence of presence. The fix is named (rarity weighting) and NOT
built, because this step's fence freezes the instrument before it is measured.
- The question texts were written during execution, after the ranker existed.
The plan recorded the gold documents' SIZE profile -- its per-row baselines
sum to 1.35 and the sizes used here reproduce that exactly, which is an
independent check that this is the set the plan profiled -- but it recorded no
question texts, and three of six gold documents could not be pinned uniquely
from the sizes. Not a blind evaluation, and the report says so.
The scorer is a tool, not a script in a document: `tools/okf_consume_measure.py`
takes the gold set as an INPUT because it is tracked in a public repository and
an answer key names a consumer's documents. hit_rank, both chance baselines and
the document-size census are unit-tested; the corpus run is a measurement.
Public-file rule, checked with a pattern DERIVED from the corpus's own 39
document names rather than hand-picked, and shown able to find first (67 hits on
the bundle's own index): zero corpus document names in any tracked file in this
repository. One leak was found and removed on the way -- a corpus concept name
in a code comment and a hardcoded corpus path in a test.
Suite run after git add: 1230 passed, mypy --strict clean on 27 files,
ruff clean.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Kjell Tore Guttormsen
d7751c0b9a