# Choosing CRMs

> bracket the deposit; few CRMs with many results

Source: https://geodb.io/guide/topic/qaqc_crm_selection/ · Category: QAQC
Which standards you insert matters as much as how many.

- **Grades must bracket the deposit.** The classic failure (Simón's
  survey): one CRM, sometimes at below-cutoff or near-detection grade,
  monitoring an ore-grade program. Recommended: **three** CRMs — near
  cut-off, near average grade, near the 95th percentile (Snowden Optiro
  says four, adding a similar-but-different matrix).
- **But too many CRMs defeats the purpose.** Rainy River used 48 unique
  gold CRMs over twelve years, and many "yielded small datasets, and their
  performance over time cannot be evaluated." **Few CRMs with many results
  beats many CRMs with few** — you cannot see a bias trend on a standard
  with six data points.
- **Keep them anonymous.** Letting the lab identify your standards — or
  telling them the expected values — is textbook poor practice. Analysts
  are human; known values get special care.
- **Mind the over-range blind spot.** If your highest CRM doesn't trigger
  the lab's overlimit method, your most economically important samples are
  running unmonitored (Rainy River found exactly this).
- **A CRM can itself be wrong.** Systematic low bias across *projects* on
  one standard is the CRM's problem, not your lab's. The discriminator is
  cross-project performance.

## In geoDB

geoDB ships a built-in **standards library** (OREAS, CDN and others, with
vetted certified values) — import the standards you use rather than
re-typing certificates. geoDB does not currently check whether your CRM
grades bracket your deposit's grades; that judgment stays with you, and
the multi-element grade statistics on your project are the input.