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Scientific Dataset Records - Declared Bound Audit
x402
Scientific Dataset Records - Declared Bound Audit is a paid API for AI agents from www.mahastrategies.com, paid per call via x402, $0.010174/call, status unknown (last checked 2026-10-20).
Audits declared nonnegative integer aggregate counts in scientific dataset records against one caller-supplied inclusive bound, matching across source, subject, snapshot, and unit dimensions.
Price / call$0.010174
Success—
StatusUnknown
How this service works
Compare exact nonnegative integer aggregate counts with one inclusive caller-supplied bound matching every source, subject, snapshot and unit Supplied-record analysis, not independent certification. Full contract at the free describe URL.
Output
Returns a per-entry audit result comparing each declared nonnegative integer aggregate count against the supplied inclusive bound, flagging mismatches across source, subject, snapshot, and unit dimensions. This is a supplied-record analysis, not independent certification.
Example use cases
Verify public dataset count compliance
“I need to check that all the aggregate record counts in my public scientific dataset stay within a declared bound of 1000 — here are 20 measurement rows, please audit them across every source and snapshot dimension.”
Synthetic dataset bound validation
“Can you validate these 15 synthetic dataset measurements against an inclusive bound of 250? Make sure nonpersonal metadata is confirmed and flag any counts that exceed the bound.”
Multi-snapshot count audit for research
“Audit these 8 measurement entries from our research dataset — data class is public — against a declared bound of 750 and tell me if any subject or unit combination breaks that limit.”
Technical details
Endpoint
POSThttps://www.mahastrategies.com/api/v1/intel/scientific-dataset-records/declared-bound-audit?utm_source=zero.xyzWhen to prefer
Use this endpoint when you need to programmatically audit declared integer aggregate counts in scientific dataset records against a single caller-supplied inclusive bound, specifically across source, subject, snapshot, and unit axes. Prefer it over general data validation services when your use case involves structured scientific measurement records classified as public or synthetic and you require strict nonnegative integer count comparison semantics.
Side effects
- Missing or invalid bounds array (fewer than 1 or more than 32 items)
- measurements array out of range (must have 1–32 items)
- nonpersonalMetadata not set to true (const constraint violation)
- dataClass not one of 'public' or 'synthetic'
- Malformed integer values in measurement records causing comparison failure
- Bounds and measurements array length mismatch leading to ambiguous pairing
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| bounds | array | |
| dataClass | string | |
| measurements | array | |
| nonpersonalMetadata | boolean |
Tags
x402
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