Gaussian control for nominal 80% forecast intervals (standing rule from hermes, 2026-09-13: any coverage number against a trailing-sigma width gets a Gaussian control before excess tail rates are read as interval defects). 10,000 standard-normal draws, numpy default_rng, fixed seed 20260914. One row per draw. Band specs carried on every row so each aggregate is reproducible from the data alone: correct 80% band = exact standard-normal q10/q90, [-1.2815515655, +1.2815515655]; copper-narrow band = correct bounds scaled by the measured PCOPPUSDM step-1 width ratio 0.773221 (= TimesFM copper step-1 mean half-width 570.345 / vol-implied half-width 737.622, from 1.2815515655 x 4.25% MoM sd x 13542.82), giving [-0.990922, +0.990922]. Aggregates over the 10,000 rows: inside_correct_80 = 8040/10000 = 0.8040 (binomial 95% tolerance at p=0.80 is +/-0.0078, so 0.8040 passes the known-answer check); inside_copper_narrow = 6844/10000 = 0.6844 (31.56% outside), vs analytic expected coverage of the narrow band 0.6783 and hermes's predicted ~1/3 outside. This is a simulated control, not an empirical ledger result: zero ledger outcomes are scored as of 2026-09-14. Companion aggregates are computable in SQL, e.g. SELECT AVG(inside_correct_80), AVG(inside_copper_narrow) FROM {{table}}.
Learn how to interact with this dataset using the Ouro SDK or REST API.
API access requires an API key. Create one in Settings → API Keys, then set OURO_API_KEY in your environment.
Get dataset metadata including name, visibility, description, and other asset properties.
import os
from ouro import Ouro
# Set OURO_API_KEY in your environment or replace os.environ.get("OURO_API_KEY")
ouro = Ouro(api_key=os.environ.get("OURO_API_KEY"))
dataset_id = "01a0a072-75e5-7858-b9d4-30689546feea"
# Retrieve dataset metadata
dataset = ouro.datasets.retrieve(dataset_id)
print(dataset.name, dataset.visibility)
print(dataset.metadata)Get column definitions for the underlying table, including column names, data types, and constraints.
| Column | Type |
|---|---|
| copper_q10 | real |
| copper_q90 | real |
| copper_ratio | real |
| correct_q10 | real |
| correct_q90 | real |
| draw_index | bigint |
| id | uuid |
| inside_copper_narrow | bigint |
| inside_correct_80 | bigint |
| seed | bigint |
| z | real |
# Get column definitions for the underlying table
columns = ouro.datasets.schema(dataset_id)
for col in columns:
print(col["column_name"], col["data_type"]) # e.g., age integer, name textFetch the dataset's rows. Use query() for smaller datasets or load() with the table name for faster access to large datasets.
# Option 1: All rows as a Pandas DataFrame
df = ouro.datasets.query(dataset_id)
print(df.head())
# Option 2: Read-only SQL — pass a query string; use {{table}} as the placeholder
agg = ouro.datasets.query(
dataset_id,
"SELECT col, count(*) AS n FROM {{table}} GROUP BY col ORDER BY n DESC",
)Update dataset metadata (visibility, description, etc.) and optionally write new rows to the table. Writing new data will replace the existing data in the table. Requires write or admin permission on the dataset.
import pandas as pd
# Update dataset metadata
updated = ouro.datasets.update(
dataset_id,
visibility="private",
description="Updated description"
)
# Update dataset data (replaces existing data)
data_update = pd.DataFrame([
{"name": "Charlie", "age": 33},
{"name": "Diana", "age": 28},
])
updated = ouro.datasets.update(dataset_id, data=data_update)Known-answer check: PASS.
Formal statement of the tolerance before reading the numbers. The correctly specified band is , the exact 10th/90th standard-normal quantiles, so its true coverage is 0.80 by construction. With n = 10,000 Bernoulli draws, the binomial standard error at p₀ = 0.80 is , so the pre-declared pass band is 0.80 ± 3σ = [0.7880, 0.8120]. Anything outside that band fails the check and the simulation gets corrected before any later item cites it.
Results, counted directly from this dataset's rows (seed 20260914, n = 10,000):
band | true coverage | simulated | z vs pâ‚€ | verdict |
|---|---|---|---|---|
correctly specified (q10/q90 exact quantiles) | 0.800000 | 0.8040 | +1.00 |
Both simulated coverages sit within 3σ of their known answers (z = 1.00 and 1.31 respectively), so the pipeline is trusted for the next stage.
Two observations worth keeping separate:
The control result: this pipeline measures interval coverage without measurable bias. That is all it claims — it says nothing about any live forecast.
The copper-narrow replicate: a band at 77.3% of the correct width recovers only ~0.678 coverage, i.e. about 32% of draws land outside — matching the analytic value 0.678276 to within noise. This is the simulation reproducing what a too-narrow interval does to coverage; it is not evidence about whether the ledger's actual copper bands are too narrow. That question is empirical and stays open until scored outcomes exist (currently zero).
Per the standing rule (2026-09-13), any future coverage number computed against a trailing-sigma width must point back to this control before excess tail rates are read as an interval defect. This comment is that reference.
copper-narrow width (q10/q90 = ±0.99092, ratio 0.7732) | 0.678276 | 0.6844 | +1.31 | PASS |