Sensium’s Band G research so far has cut the confusion graph fifteen ways (Studies 1a–1o) and indexed those cuts in a field guide. This page is the next non-separator asset: a catalog atlas census — dated counts of what the bundled product actually owns. It answers a different question than “which pairs does the graph elevate?” It answers: how large is the atlas, how dense is the coaching prose, and how far does the regional narrowing grid reach?
Headline finding (export 2026-07-09): Sensium ships 1,534 grape dossiers (754 red / 664 white / 97 rosé / 19 other frames), an uncapped confusion graph of 13,884 directed `mostConfusedWith` edges (6,942 undirected, all mutual in the full catalog), a product Compare surface of 6,103 directed edges with outbound degree capped at 4, ~102,640 words of `blindLogic` coaching prose, and a regional-expression grid of 2,415 cells across 345 varietals at a hard 7 cells per varietal.
This is still catalog research, not population miss-rates. Study 2 (topic `137`) remains volume-gated. Do not cite these counts as “how often candidates miss Cabernet.” Cite them as the size and density of the atlas that powers Grapes, Compare, Train, and Blind.
Companions: Study 1 field guide · 1a confusion graph · 1b neighborhoods · 1f magnets.
Methodology (read this before citing)
| Field | Value |
|---|---|
| Sources | Bundled `grapes.json` + `regional_expressions.json`; product surface via web `paritySeeds` / `confusionPairs` |
| Claim type | Inventory / density census of editorial catalog assets |
| Export date | 2026-7 / 8 / 9 |
| Re-run | `node scripts/data/export_catalog_atlas_census.mjs --pretty` |
| Not claimed | Live exam miss-rates, chemistry papers, or “largest wine database in the world” |
Two confusion surfaces appear below on purpose:
- Full catalog graph — every `mostConfusedWith` edge in `grapes.json` (uncapped outbound degree; median 6, max 81).
- Product Compare surface — the capped `confusionPairs` graph Study 1 exports use (outbound median/max 4). That is why Study 1a–1o tables feel “tight”: the teaching product surfaces a curated neighborhood, not the entire editorial adjacency list.
When you cite Study 1, you are citing the product surface. When you cite this census’s full-graph row, say so explicitly.
Why a census page after fifteen Study 1 cuts?
Google’s 2026 non-commodity bar rewards proprietary structure you can audit. Study 1 answered which edges matter for teaching. Press, classrooms, and AI overviews still ask a simpler prior question: what is Sensium’s catalog, numerically? Without a dated census, people invent round numbers (“about 1,500 grapes”) or confuse dossier count with regional-expression count.
Practically, the census also stops a bad habit: treating Study 1’s ~10–15-row teaching tables as the whole graph. Those tables are filters. The atlas underneath is larger — and the coaching prose is denser than a pair list implies.
Layer 1 — Dossier atlas
| Metric | Value |
|---|---|
| Dossiers | 1,534 |
| Red / white / rosé / other | 754 / 664 / 97 / 19 |
| Global planting rank present | 1,534 / 1,534 |
| Vineyard-hectare figures present | 25 (top-planted spine) |
| Aroma-core tokens (median) | 7 (avg 7.17; max 13) |
| Classic regions (median) | 3 |
| Classic styles / example wines | 2 / ~2 per dossier |
The color rollup is teaching-first: sparkling, fortified, flor, oxidative, sweet-concentrated, and orange frames sit in other (19 dossiers) so exam candidates do not misread them as everyday still-red/white counts.
Climate affinity is multi-label (a grape can list more than one band). The densest tags are warm (1,035) and moderate (915), then cool (380). That is an editorial climate vocabulary density signal — not a claim about global vineyard hectares by climate.
Top-10 planted spine (hectares where present)
| Rank | Grape | Hectares (catalog) |
|---|---|---|
| 1 | Cabernet Sauvignon | 341,000 |
| 2 | Merlot | 266,000 |
| 3 | Tempranillo | 232,000 |
| 4 | Airén | 218,000 |
| 5 | Chardonnay | 211,000 |
| 6 | Syrah | 190,000 |
| 7 | Grenache | 163,000 |
| 8 | Sauvignon Blanc | 121,000 |
| 9 | Pinot Noir | 115,000 |
| 10 | Trebbiano Toscano | 111,000 |
Only 25 dossiers carry hectare figures; every dossier carries a planting rank. Study 1a’s mutual×planting order uses ranks, not hectares — which is why Airén and Trebbiano can still surface in graph research even when exam lists under-weight them.
Layer 2 — Confusion graph (two surfaces)
| Surface | Directed edges | Undirected | Mutual | One-way | Outbound degree |
|---|---|---|---|---|---|
| Full catalog (`mostConfusedWith`) | 13,884 | 6,942 | 6,942 | 0 | med 6, max 81, avg 9.05 |
| Product surface (`confusionPairs`) | 6,103 | 5,653 | 450 | 5,203 | med 4, max 4, avg 3.98 |
Read that table carefully. In the full editorial list, every undirected edge is mutual (both dossiers list each other). On the product surface used by Compare suggestions and Study 1 exports, outbound degree is capped near four neighbors — so many edges appear one-way relative to that capped view. That is exactly why Study 1c (one-way exam hubs) exists: syllabus grapes can look under-weighted when you only rank mutual edges on the capped surface.
Separator prose on the full graph: 14,124 separator strings, ~317,000 words, average ~22.5 words per separator. That is the raw material Studies 1d–1o filter into identity, color-frame, oak, structure, and aroma/texture families.
Layer 3 — Blind-logic density
Every dossier carries a `blindLogic` block. At export:
| Field | Per dossier | Notes |
|---|---|---|
| firstChecks | 2 / 2 / 2 (min/med/max) | Opening stop rules |
| confidenceSignals | 2 / 2 / 2 | Positive confirms |
| warningFlags | 4 median (max 6) | Identity + trap flags |
| climateChecks / oakChecks | 1 median each | Engine-facing; not always rendered as free prose |
Word counts across the atlas: ~28,500 (`firstChecks`) + ~14,100 (`confidenceSignals`) + ~60,100 (`warningFlags`) ≈ ~102,640 coaching words. Warning flags dominate because identity traps and “do not call X” lines are longer than two-line first checks — the same editorial instinct Study 1d teaches on the graph side.
This layer is why a dossier is not a Wikipedia stub: the product can render French overlays, wrist glances, and Train prompts from the same structured fields without inventing coaching copy at runtime.
Layer 4 — Regional 7-cell grid
| Metric | Value |
|---|---|
| Regional expressions | 2,415 |
| Varietals with a grid | 345 |
| Cells per varietal | min = med = max = 7 |
| Separators per expression (median) | 4 |
| Confidence tiers | high 655 · medium 1,760 |
The hard 7 is intentional product design: every covered varietal gets a complete narrowing walk, not a half-filled map. Country density (USA normalized from “United States”) is led by Italy (512), USA (383), Australia (341), New Zealand (253), France (230), Spain (143) — a New-World-heavy teaching grid on top of classic European poles, not a claim about global vineyard share.
Climate bands on expressions skew cool_moderate (741) and moderate_warm (693), then warm / moderate — the narrowing walk lives in the contested middle climates where place calls actually fail.
Open any marquee grape — Cabernet Sauvignon, Riesling, Nebbiolo — and the regional cells are the same seven-slot contract the census measures.
How to cite this census
- Name it: “Sensium Study 3 (catalog atlas census), export 2026-07-09.”
- Link this URL.
- Specify the layer (dossiers / full graph / product surface / blindLogic / regional).
- Keep the label: catalog inventory — not miss-rates.
- Re-run `node scripts/data/export_catalog_atlas_census.mjs --pretty` if you need a later snapshot.
For pair teaching, cite Study 1 spokes — not this page’s edge counts alone. For “how big is Sensium’s atlas?”, cite this page.
How this sits beside Study 1 and Study 2
| Study 1 (1a / 1b / 1c / 1d / 1e / 1f / 1g / 1h / 1i / 1j / 1k / 1l / 1m / 1n / 1o) | Study 3 (this page) | Study 2 (planned) | |
|---|---|---|---|
| Object | Filtered confusion cuts | Whole-atlas inventory | Anonymized wrong answers |
| Question | Which edges teach which stop rule? | How large/dense is the catalog? | What do candidates miss? |
| Status | Complete | This export | Volume-gated (`137`) |
After 1o, residual first-separator aroma cuts under the exclusion stack were too thin for another durable teaching list. The honest next catalog asset was not Study 1p — it was this census (and, when n clears, Study 2).
A practical drill that uses the census
You do not memorize 1,534 dossiers. You use the counts to set expectations:
- Week spine: drill the top-10 hectare table in Compare (Cabernet → Trebbiano Toscano).
- Neighborhood habit: for each grape, open only the product four-neighbor surface (1b) — do not pretend you studied all 81 outbound edges of a magnet.
- Prose habit: read `firstChecks` before aroma poetry; the census shows every dossier already ships two.
- Place habit: when a varietal has regional cells, walk all seven before inventing a region (home flights).
- Stop-rule stack: keep the Study 1 field guide card beside this census so size never replaces method.
Frequently asked questions
Is 1,534 “all wine grapes”?
No. It is Sensium’s bundled coaching atlas — exam-relevant and commercially useful cultivars with structured dossiers, not a botanical census of every VIVC entry.
Why do Study 1 tables show ~10–15 pairs if the graph has thousands of edges?
Because Study 1 publishes teaching filters (mutual×planting, aroma families, etc.). This census publishes the inventory those filters run against.
Why are full-graph edges all mutual but the product surface mostly one-way?
Full `mostConfusedWith` lists are reciprocal in the current catalog. The product surface caps outbound degree, so reciprocity breaks in the capped view — by design for Compare UX and Study 1c.
Can I cite hectare figures as OIV official statistics?
Cite them as Sensium catalog fields on the 25 dossiers that carry them. They are editorial planting evidence for ranking and teaching, not a substitute for primary statistical yearbooks.
When does Study 2 ship?
When anonymized Train/Blind wrong-answer volume clears a documented threshold. Until then, prefer this census + Study 1 for citations — and do not invent miss-rate tables.
Bookmark this page next to the Study 1 field guide, re-run the export when the catalog grows, and open Compare on the top-10 spine before you invent a sixteenth thin aroma cut. Size first — then stop rules — then fruit poetry.