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Optical Measurements Reference Integrity Map
x402
Optical Measurements Reference Integrity Map is a paid API for AI agents from www.mahastrategies.com, paid per call via x402, $0.035209/call, status unknown (last checked 2026-10-16).
Binds optical measurement rows to a supplied dataset/material/lot/revision record to verify reference integrity, without performing independent certification.
Price / call$0.035209
Success—
StatusUnknown
How this service works
Bind measurement rows to the exact supplied dataset/material/lot/revision/content Supplied-record analysis, not independent certification. Full contract at the free describe URL.
Output
A reference integrity map indicating which measurement rows are successfully bound to the supplied dataset/material/lot/revision record, along with flags for any rows that are missing, mismatched, or inconsistent with the declared reference — without issuing independent certification.
Example use cases
Lab dataset reference audit
“I have a batch of optical measurement rows from our spectrometry run — can you bind them to lot ID 'LOT-2024-88' in our synthetic dataset and verify the reference integrity so I know which rows match the declared revision?”
Manufacturing QC measurement binding
“We need to confirm our optical measurement records are correctly linked to the exact material lot and revision used in production — please run a reference integrity map on these 20 rows against the supplied lot, data class is public.”
Research dataset consistency check
“Before we publish, can you validate that all our optical measurement rows are properly anchored to the declared dataset reference and material lot, using synthetic data class with non-personal metadata only?”
Technical details
Endpoint
POSThttps://www.mahastrategies.com/api/v1/compute/optical-measurements/reference-integrity-map?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to deterministically bind and verify optical measurement rows against a caller-supplied reference record (dataset, material, lot, revision) rather than perform independent lab certification or generate new measurements. Ideal for quality assurance workflows where the source record is already declared and you need to confirm row-level reference integrity.
Side effects
- Rows or lots array exceeds 32 items — request rejected
- dataClass not one of 'public' or 'synthetic' — validation error
- nonpersonalMetadata not set to true (const) — request rejected
- Mismatched lot or dataset identifiers — integrity map shows unbound rows
- Empty rows or lots arrays — minimum item count violation
- Network or payment (x402) failure — no result returned
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| lots | array | |
| rows | array | |
| dataClass | string | |
| nonpersonalMetadata | boolean |
Tags
x402
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