Choosing CRMs

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.