proposed, the gain values used for calibration may vary by machine to account for any variation. This should allow for sensors from different machines to yield the same calibrated readings. As a result of this methodology, a temperature cal- ibration with an accuracy of 1.3 to 6.1 is realized using bichromatic Planck thermometry. In addition, trends in data variation as a function of temperature are analyzed, showing that with a constant sensor gain, the standard deviation of sensor readings decreases with increasing temperature. It is shown that this change in the standard deviation of predicted temperatures is not solely due to the source temperature, but to the amount of the signal being reduced at lower source temperatures. It is experimentally validated that low standard deviation readings can be achieved at lower temperatures by increasing the signal. As well, a software framework is developed for reducing spatial deviations in the temperature measured. 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