# Calibration Court Recalibrate

> Calibration Court Recalibrate is a paid API for AI agents from calibration-court.onrender.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-29).

Maps a model's raw output probability to an empirically calibrated probability using resolved outcomes stored on the ledger

## Facts

- Endpoint: POST https://calibration-court.onrender.com/api/v1/recalibrate?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-29
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/calibration-court-recalibrate-fbf2d0f5
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_FtxtejLMMg4GxWxeAY-X_

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability calibration-court-recalibrate-fbf2d0f5 -d '<json body>'
```

Example prompt: My GPT-4 binary classifier returned a raw probability of 0.82 for a fraud detection task — can you recalibrate that score using resolved ledger outcomes for model 'gpt-4' and task type 'fraud_detection'?

## When to prefer this

Use this endpoint when you have a raw probability from a specific ML model and want it adjusted to reflect empirical accuracy rather than the model's uncalibrated confidence. Ideal when deploying models in high-stakes contexts (medical, financial, forecasting) where calibration error is costly, and when resolved ground-truth outcomes for the model are available on the ledger. Prefer this over generic isotonic fitting endpoints when you do not have your own labelled outcome pairs and want to leverage shared resolved outcomes.

## Known failure modes

- No resolved outcome data available for the specified model and task_type combination — endpoint may refuse or return an error
- Raw probability out of [0,1] range — validation error
- Unknown or unsupported model identifier — endpoint refuses the request
- Insufficient historical calibration data for reliable adjustment
- Payment failure via x402 — request rejected before processing

## How this service works

Paid (x402, USDC on Base). Map a model's raw probability to an empirically calibrated one using resolved outcomes on the ledger. Refuses wit

## Output

A calibrated probability value (between 0 and 1) that has been adjusted based on empirically resolved outcomes stored on the ledger, replacing the raw model-output probability with one that better reflects real-world frequencies for that model and task type.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "prob": {
   "type": "number",
   "maximum": 1,
   "minimum": 0
  },
  "model": {
   "type": "string"
  },
  "task_type": {
   "type": "string"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/calibration-court-recalibrate-fbf2d0f5/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from calibration-court.onrender.com](https://www.zero.xyz/host/calibration-court.onrender.com/llms.txt)