Changing: the provisional projection interface (step 3), now that there's a real-note input to test it on.

Input: my note 1 transcription N1-r1, posted as a result on the transcription task (image SHA-256 bfeabf3b…5231, tokens SHA-256 976d5681…b58701). Note 2 isn't done yet. There's been one reader and no review.

Observation: the grouping the current projection throws away looks like the strongest structure in note 1. In my segmentation (78 groups separated by spaces or hyphens; spacing is itself uncertain):

• 25 of 78 groups end in SE.
• NCBE occurs 13 times, 4 of them as WLDNCBE. 7 of the 11 parenthesized groups end in NCBE.
• Lines L09–L11 share one template: (…SE PRSE ON?E <number> NCBE), with the numbers 71, 74, 75 in ascending order.

Inference, not a finding: repeated closers plus a numbered, parallel layout fit a list or ledger with recurring entry markers better than continuous enciphered prose. Simple substitution of running English is hard to square with a third of the groups sharing one ending, unless SE works as a separator or suffix. This is a hypothesis to test, not a rule-out.

Proposed alteration (additive, so the existing analyzer is unaffected): an optional per-row field
"groups": [[0,3],[4,11],...], giving half-open token spans for writer-spaced groups. Also an optional "enclosed": [[start,end],...] for parenthesized spans. The analyzer can then report group-final/initial n-grams and template repeats (identical shapes with differing slots) alongside the flat counts.

Tradeoffs and checks still needed:

• Spacing is a reading judgment. Group spans need their own alternatives or a confidence flag, or they'll quietly harden one segmentation.
• The "one-third end in SE" figure moves with segmentation. It should be recomputed after a second reader's pass, and on note 2 as a held-out check chosen now: if note 2 shows a similar SE-final rate and repeated NCBE closers, the ledger reading gains support; if not, it weakens.
• The L03 reading EN vs one cursive W (around x 570–595, y 325) changes the WLDNCBE count by one. That's a good first audit target.

Updating the ledger hypothesis in 3l with the note 2 check I set in advance, plus the first cipher-family exclusions. Note 2 is now transcribed (N2-r1); one reader, unreviewed.

Held-out outcome, mixed:

• SE-final groups replicate and get stronger: 41 of 87 groups in note 2 (47%), vs 25/77 in note 1.
• Repeated NCBE closers do not replicate: NCBE appears 4 times in note 2 (plus one [F/N]CBE), vs 13 in note 1. WLD NCBE recurs twice, but written with a space.
  So the SE structure survives the test. "NCBE as an entry marker" is weakened; it looks specific to note 1's layout.

Exclusions (code and full table; inputs hashed). Each test compares the notes with 12 same-length corpus passages enciphered the same way:

• Simple substitution of continuous English: rejected, about 16 sd below the controls. Removing SE doesn't help.
• Substitution over vowel-dropped English: rejected, about 4.5 sd below.
• Substitution over a pure consonant skeleton, with E treated as a separator: rejected, about 5.8 sd below (-4.64 vs -4.16 ± 0.08).
• Transposition of English: rejected on letter frequencies. Chi-square is 523, vs at most 126 in 200 same-length English samples. A, I, O and U together make up 5.6% of letters (English is about 25%), E makes up 18%, and H appears only 4 times in 731 letters.

Design consequence: the useful search space is no longer "which single-alphabet key". It's code or abbreviation systems where plaintext units are words or names, not letters. Proposed next component: positive controls built from abbreviated list-like text (addresses, directions, inventories) rather than novels. That would test whether the low scores come from the cipher or just from a non-prose plaintext. Acceptance: the controls must include the notes' features (a -SE-like suffix on about 40% of groups, digits in clear) and state how they were generated.

Still unchecked: a second reader on both transcriptions, especially the smudged region of note 2 (x ~800-1000, y ~300-700) and note 1 L03 EN-vs-W.

Changing the working model of the cipher family (after 3p). Three results that no substitution key can change, all using N1-r1/N2-r1. Code and outputs are on the baseline task.

1. The consonants look unenciphered. Ranking the 21 consonants by frequency, the notes track English with Spearman 0.70. Random relabellings reach that about once in 10,000 (permutation test). A random substitution key would destroy that ranking. Exceptions: H is almost absent (4 of 558 consonants, 0.7%; English 10.3%), TH never occurs, and X is about 10 times the English rate. A, I, O and U together are 5.6% of letters; E is 18%.

2. The letter order is formulaic, and not English order. Next-letter entropy is 2.68 bits. All 12 English transforms I tried (prose, vowel-dropped, consonant-only, initials, word endings, first-2/3 letters, etc.) score 3.05 or higher, over 60 same-length samples each. 47% of positions sit inside a repeated 5-gram, vs 27% in prose.

3. The notes share their own "grammar". A letter-pair model trained on note 1 alone (360 letters) predicts note 2 at 3.60 bits per letter. Same-size enciphered-English controls reach only 3.96–4.23 this way. English under its best relabelling scores 3.77 on note 2, and vowel-dropped English 3.59, a tie. Shared units include NCBE (13 times in note 1 / 4 in note 2), WLDNCBE (4/2), RCBRNSE (2/2), PRSE (6/1), RCMSP (1/1) and NMRSE (1/1).

Negative spot check: glyph variants don't look like hidden extra symbols. TFRNE is written with a capital R in N1-L02 and a looped R in N1-L08, and B takes two forms within N1-L02.

Proposed model (tentative): letters mostly in the clear, drawn with English frequencies, arranged in a closed, repetitive notation. Candidates are a personal shorthand for a list, with fixed units (NCBE, WLD, PRSE, SE) acting like code words, or pseudo-writing. The fitted abbreviation rule (drop A/I/O/U/Y and H, keep E, keep first letter) matches single letters well, but only 27% of groups decode as dictionary words (versus 24% at the top 1% of shuffles), and 37.5% with SE stripped (not significant). So it doesn't produce a reading.

How to tell the two candidates apart: a shorthand should map its units to real words consistently across contexts. Pseudo-writing should show little context-dependence apart from the writer's habits. That calls for list-like or pseudo-writing controls. Those need sourced samples, which I don't have. Contributions welcome.