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Neural Interface Records - Condition Coverage Map
x402
Neural Interface Records - Condition Coverage Map is a paid API for AI agents from www.mahastrategies.com, paid per call via x402, $0.035228/call, status unknown (last checked 2026-10-16).
Analyzes synthetic binary window-count data within a declared model/calibration/pipeline frame to compute false-command-window fraction and intention missed-window rates.
Price / call$0.035228
Success—
StatusUnknown
How this service works
Check synthetic binary window-count accounting in one declared model/calibration/pipeline frame and derive exact rest false-command-window fraction and intention missed-window Supplied-record analysis, not independent certification. Full contract at the free describe URL.
Output
Returns computed condition coverage metrics for the supplied synthetic neural interface records within the declared model/calibration/pipeline frame, including the exact rest false-command-window fraction and intention missed-window rate. Results reflect supplied-record analysis only, not independent certification.
Example use cases
BCI pipeline calibration audit
“I have 20 rows of synthetic binary window-count data from our BCI simulation 'run-2024-09-alpha', model digest 'mdl-9f3a', calibration digest 'cal-44bc', pipeline revision 'v2'. Can you compute the condition coverage map and tell me the false-command-window fraction and intention missed-window rate?”
Neural interface model validation
“We need to validate the condition coverage for our neural interface model — simulation ref 'sim-nir-007', model digest 'mdl-delta-88', calibration digest 'cal-gamma-12', pipeline revision 'rev-5'. I'm sending 15 rows of synthetic window data. What's our rest false-command fraction?”
Pre-deployment pipeline coverage check
“Before we deploy the next revision of the neural interface pipeline, run a window-count accounting check on these 8 synthetic rows using dataset ref 'ds-prerelease-v4', model digest 'mdl-prerel-001', calibration digest 'cal-prerel-001', and pipeline revision 'v4-rc1'. I need to confirm the missed-window and false-command metrics are within bounds.”
Technical details
Endpoint
POSThttps://www.mahastrategies.com/api/v1/compute/neural-interface-records/condition-coverage-map?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to compute binary window-count condition coverage metrics (false-command-window fraction, intention missed-window rate) for synthetic neural interface records within a specific, declared model/calibration/pipeline frame. It is purpose-built for BCI/neural interface simulation analysis and is not intended for real patient data or independent certification.
Side effects
- Invalid or mismatched modelDigest or calibrationDigest returns an error indicating frame mismatch
- Rows array exceeding 32 items or below 1 item is rejected by schema validation
- dataClass not set to 'synthetic' or nonpersonalMetadata not set to true causes request rejection
- Missing required references (datasetRef, simulationRef, pipelineRevision) results in incomplete frame error
- Malformed row data causes computation failure with data format error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| dataClass | string | |
| datasetRef | string | |
| modelDigest | string | |
| simulationRef | string | |
| pipelineRevision | string | |
| calibrationDigest | string | |
| nonpersonalMetadata | boolean |
Tags
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