What this work contributes
Analyzes alternating measurements of two configurations and removal of polynomial zero-point drift. It explicitly treats the statistical uncertainty introduced by drift filtering, helping distinguish a corrected estimate from an artificially clean trace.
Where the evidence stops
Drift subtraction depends on the chosen model and sequence. It cannot remove every disturbance or establish that an observed difference comes from the proposed physical mechanism.
Keep the conditions with the result
- Measured / modeled quantity
- Difference between alternating measurement states and its uncertainty
- Reported scale
- Not a force-sensitivity result; statistical cost depends on sample count and filter order.
- Conditions
- Analytical treatment of time-ordered comparison measurements.
- Evidence context
- Method paper; hardware replication is not the reported result.
- Design use
- Predefine on/off sequences, drift model and uncertainty calculation for MF-01.
- Review access
- Author abstract and journal DOI inspected; method not independently rederived here.
Original source
H. Erik Swanson, Stephan Schlamminger. Removal of zero-point drift from AB data and the statistical cost. Measurement Science and Technology 21, 115104 (2010).
DOI: 10.1088/0957-0233/21/11/115104
How we use it
This record belongs to Measurement & replication. Its evidence label describes the cited work, not the maturity of a Metric Flight device. Follow the original methods, corrections and conditions before transferring a result to a different geometry or scale.
Editorial note reviewed 19 October 2026. AI assisted research discovery and synthesis. Citation metadata and claims were checked against primary publication or institutional records; full-text access varies. Read the source for the complete method.