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Catalyst Measurements - Missingness Co-occurrence
x402
Catalyst Measurements - Missingness Co-occurrence is a paid API for AI agents from www.mahastrategies.com, paid per call via x402, $0.020318/call, status unknown (last checked 2026-10-16).
Counts paired missing owner, revision, and content pins in catalyst measurement records without treating missing values as matches for public material-lot/method-pinned data.
Price / call$0.020318
Success—
StatusUnknown
How this service works
Count paired missing owner, revision and content pins without silently treating a missing value as a matching value for public material-lot/method-pinned catalyst measurement Supplied-record analysis, not independent certification. Full contract at the free describe URL.
Output
Returns counts of paired missing pin occurrences (owner, revision, content) across the supplied catalyst measurement rows, showing which pin combinations are simultaneously absent and how often, without treating missing values as matching values.
Example use cases
Audit catalyst record pin completeness
“I have 20 public catalyst measurement records and I need to find out which ones are simultaneously missing both owner and content pins — can you count the co-occurring missingness without treating blanks as matches?”
Validate synthetic dataset pin coverage
“Run a missingness co-occurrence check on these 15 synthetic catalyst measurement rows and tell me how many records are missing paired revision and content pins at the same time.”
Flag provenance gaps before submission
“Before I submit this batch of 30 public catalyst measurements, check them for paired missing owner and revision pins so I can fix the gaps — I don't want silent nulls treated as valid matches.”
Technical details
Endpoint
POSThttps://www.mahastrategies.com/api/v1/compute/catalyst-measurements/missingness-cooccurrence?utm_source=zero.xyzWhen to prefer
Use this endpoint when you need to explicitly count co-occurring missing pin pairs (owner, revision, content) in catalyst measurement records and require that missing values are never silently treated as matches. Prefer this over generic null-checking tools when your data involves material-lot or method-pinned scientific measurements where provenance integrity matters.
Side effects
- rows array exceeds 32 items — returns validation error
- rows array is empty (minItems:1 violated) — returns schema error
- dataClass not set to 'public' or 'synthetic' — returns enum validation error
- nonpersonalMetadata not set to true (const:true violated) — returns constraint error
- malformed row objects lacking expected pin fields — may return incomplete or zero counts
- network timeout for large row batches — returns HTTP 408 or gateway error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| dataClass | string | |
| nonpersonalMetadata | boolean |
Tags
x402
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