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Publication Records - Unit-Label Consistency Check
x402
Publication Records - Unit-Label Consistency Check is a paid API for AI agents from www.mahastrategies.com, paid per call via x402, $0.012332/call, status unknown (last checked 2026-10-16).
Validates that declared aggregate count kinds match their unit labels within a single specified source, work, study, or revision record.
Price / call$0.012332
Success—
StatusUnknown
How this service works
Check declared aggregate count kinds against unit labels in one exact source/work/study/revision Supplied-record analysis, not independent certification. Full contract at the free describe URL.
Output
The endpoint returns an analysis of whether the declared aggregate count kinds in the supplied records are consistent with their unit labels, identifying any mismatches or inconsistencies within the single specified source, work, study, or revision. This is a supplied-record analysis, not independent certification.
Example use cases
Pre-submission dataset unit audit
“Before I submit this research paper, can you check these 15 public measurement records and flag any mismatches between the declared aggregate count kinds and their unit labels? The metadata is all non-personal.”
Synthetic data pipeline validation
“I've generated some synthetic measurement data for testing — can you run a unit-label consistency check on these 8 measurements to make sure the count kinds match up with the unit labels before I use them downstream? Mark it as synthetic and non-personal.”
Revision record consistency review
“We just updated a study revision and I need to verify the unit-label consistency for these 20 public measurement records — can you check that none of the aggregate count declarations conflict with their unit labels? All metadata is non-personal.”
Technical details
Endpoint
POSThttps://www.mahastrategies.com/api/v1/intel/publication-records/unit-label-consistency?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to validate unit-label consistency within a single publication record, study, or revision and your data is pre-scoped to one source. It is suited for pre-submission audits, pipeline quality checks, and revision reviews where the goal is catching count-kind/unit-label mismatches in research or scientific datasets. It is not an independent certification service — use it as a lightweight analytical check on your own supplied records.
Side effects
- Missing or invalid dataClass enum value (must be 'public' or 'synthetic')
- measurements array outside 1–32 item bounds returns validation error
- nonpersonalMetadata not set to true (const:true required)
- Malformed measurement objects in the array
- Ambiguous or unrecognizable unit labels in input data
- Network timeout on large measurement arrays
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| dataClass | string | |
| measurements | array | |
| nonpersonalMetadata | boolean |
Tags
x402
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