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Scientific Dataset Records - Unit Label Consistency Check
x402
Scientific Dataset Records - Unit Label Consistency Check is a paid API for AI agents from www.mahastrategies.com, paid per call via x402, $0.012338/call, status unknown (last checked 2026-10-20).
Checks declared aggregate count kinds against unit labels within a single scientific dataset record to identify inconsistencies.
Price / call$0.012338
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
Returns an analysis of whether the declared aggregate count kinds in the supplied measurements are consistent with their unit labels, flagging any mismatches found within the single record provided. This is a supplied-record analysis, not independent certification.
Example use cases
Research Dataset Audit Before Publication
“I have a set of 15 public measurements from a study I'm about to publish — can you run a unit label consistency check on them to make sure none of the declared aggregate count kinds conflict with their unit labels?”
Synthetic Data Pipeline Quality Check
“We're generating synthetic scientific datasets and I need to validate that the unit labels on these 20 measurements are consistent with their declared aggregate count kinds before we ship them downstream.”
Study Revision Pre-Submission Validation
“Before I submit this revised study record, can you check these 10 public measurements for any mismatches between declared count types and unit labels so I can catch errors early?”
Technical details
Endpoint
POSThttps://www.mahastrategies.com/api/v1/intel/scientific-dataset-records/unit-label-consistency?utm_source=zero.xyzWhen to prefer
Use this endpoint when you need a quick, per-record consistency check between aggregate count kind declarations and unit labels in a single scientific dataset, study, or revision. Best suited for pre-publication audits, pipeline quality gates, or revision validation where the data is either public or synthetic and confirmed non-personal.
Side effects
- measurements array empty or exceeds 32 items — rejected by schema validation
- nonpersonalMetadata not set to true — request rejected
- dataClass not one of 'public' or 'synthetic' — enum validation failure
- malformed measurement objects — partial or full analysis failure
- payment not completed — 402 response blocking access
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| dataClass | string | |
| measurements | array | |
| nonpersonalMetadata | boolean |
Tags
x402
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