Sweet deceit, and Just Deserts: The Red Pine Assay Scandal
As many CEOs will tell you, particularly at the conferences we have been to over the last few years focusing on the junior end of the market, money has been tight. As an industry, we have made great strides in standardizing our work and establishing a formal, rigid framework for reporting what’s in the ground. However, there are very few tested safeguards against bad actors, and we rely heavily on the strength of our technical teams to identify and address anomalies.
The recent case of Red Pine Exploration is a stark reminder of this vulnerability. Former CEO Quentin Yarie has had a significant impact, shocking the public market with another mining scandal. Although not on the scale of Bre-X, it is still astonishing that someone would manipulate data in this way. In an age of advanced 3D visualization programs that allow us to fully appreciate the ore body in situ, discrepancies between lab results and geological structures should be obvious.
To understand why changing a few results can have a large effect, here’s a basic overview of what the math is doing behind the scenes:
Understanding Kriging with a Simple Analogy and the Red Pine Exploration Scandal
Imagine you are making cookie dough and you want to know the sweetness level (sugar content) throughout the entire batch. However, you don't have the time or resources to test every part of the dough. Instead, you take small samples from different parts of the dough to measure the sweetness.
What is Kriging?
Kriging is like predicting the sweetness of the entire batch of cookie dough based on the few samples you've tasted, using some smart mathematical techniques.
Key Concepts with an Analogy
Spatial Continuity (Sweetness Spread):
Think of it like tasting samples of cookie dough that are close together. If one sample is sweet, the one right next to it is probably also sweet. The further apart the samples are, the less certain you are that they'll have the same sweetness.
Variogram (Sweetness Map):
Imagine creating a map that shows how much the sweetness varies depending on how far apart the samples are. This map helps you understand the "sweetness relationship" across the entire batch of dough.
Weighted Average (Smart Guessing):
To guess the sweetness of an unsampled part of the dough, you look at the samples around it. If most of the nearby samples are sweet, you guess that the unsampled part is also likely sweet. The closer a sampled part is to the unsampled one, the more weight (importance) it gets in your guess.
Types of Kriging (Different Guessing Methods)
Ordinary Kriging (Common Guessing):
You assume the overall sweetness is constant but unknown and use your sampled dough to make the best guess.
Simple Kriging (Simplified Guessing):
You assume you already know the average sweetness of all the dough and make guesses based on that known average.
Universal Kriging (Trend Guessing):
You notice a trend, like the dough on the edges of the bowl being less sweet, and use this trend in your predictions.
Indicator Kriging (Yes/No Guessing):
You categorize samples as either sweet or not sweet and predict the presence or absence of sweetness.
The Impact of Modifying Sample Points
Imagine if you only tasted samples from the middle of the dough and ignored the edges. Your prediction of the overall sweetness would be biased, thinking all the dough is as sweet as the middle. This can lead to:
Overestimation or Underestimation: You might think the entire batch is sweeter (or less sweet) than it actually is.
Poor Decisions: If you decide how much sugar to add based on these wrong guesses, you might end up with dough that’s too sweet or not sweet enough.
Real-World Example: Red Pine Exploration
Red Pine Exploration recently faced a significant scandal involving the manipulation of assay results, which are critical in estimating the quantity and quality of mineral deposits. Quentin Yarie, the former CEO, was implicated in altering data to present overly optimistic results about the Wawa Gold Project. This manipulation is akin to altering the sweetness measurements of only a few samples of the cookie dough, leading to an overall inflated estimation of the dough's sweetness (gold content). None of the allegations against Mr. Yarie have been proven in court, and the presumption of innocence must be maintained until proven otherwise.
The consequences of this scandal included:
Reputation Damage: The company's credibility took a major hit, affecting its standing in the market and with investors (Junior Mining Network) (The Northern Miner).
Financial Repercussions: Share prices dropped significantly, reflecting the market's reaction to the news of data manipulation (Red Pine Exploration).
Operational Delays: The need to re-evaluate and correct the assay results caused delays in project planning and execution (Red Pine Exploration) (Red Pine Exploration).
Red Pine has since taken steps to address these issues, including appointing a new interim CEO and conducting thorough reviews of their assay data to restore accuracy and trust (Red Pine Exploration) (Junior Mining Network).
This case highlights the importance of maintaining integrity in resource estimation processes. Accurate and honest reporting is essential to ensure reliable resource estimates and to build trust with stakeholders.
If you need expert advice on recruitment in the mining sector, contact us today to ensure you have the best technical teams in place to uphold these standards.