An automated deep-sea telemetry system logs sonar depth measurements during a research cruise.

# ----- Global Constants & State -----
SONAR_READINGS = [124.5, 128.2, 119.0, 131.4, 122.8]

# ----- Utility Functions -----
def processDepthData(readingsList):
    cleaned = [r for r in readingsList if r > 0]
    avg_depth = sum(cleaned) / len(cleaned)
    variance = sum((x - avg_depth) ** 2 for x in cleaned) / len(cleaned)
    standard_dev = variance ** 0.5
    return round(standard_dev, 2)

# ----- Mission Control -----
mission_active = True
if mission_active:
    dispersion = processDepthData(SONAR_READINGS)
    print("Telemetry dispersion metric:", dispersion)

Identify the computational thinking technique employed when the mathematical complexity of cleaning data, calculating the variance, and computing the standard deviation is encapsulated within processDepthData(), thereby allowing the main mission control segment to retrieve the dispersion metric without managing these mathematical steps.