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Propagation of Uncertainty from Random Error00:59

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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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Instrument calibration is essential for ensuring that instruments produce accurate and consistent results. It is vital in manufacturing, healthcare, testing laboratories, and scientific research. Calibration processes are specific to each instrument and help enhance data accuracy. Each instrument has a unique calibration process tailored to its design and function to improve data accuracy.
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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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Bayesian calibration, process modeling and uncertainty quantification in biotechnology.

Laura Marie Helleckes1,2, Michael Osthege1,2, Wolfgang Wiechert1,3

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Summary
This summary is machine-generated.

This study introduces a new computational approach for non-linear calibration modeling, enhancing data quality and uncertainty quantification in life sciences. The developed Python packages, calibr8 and murefi, improve reproducibility and automation in experimental data analysis.

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Area of Science:

  • Experimental sciences
  • Life sciences
  • Biotechnology

Background:

  • High-throughput experimentation generates large datasets, necessitating robust data analysis.
  • Accurate quantification and uncertainty estimation are crucial for reliable experimental results.
  • Non-linear calibration modeling is essential for extracting quantitative information in life sciences.

Purpose of the Study:

  • To present a conceptual and computational framework for non-linear, empirical calibration modeling.
  • To develop accessible Python packages for uncertainty quantification in calibration.
  • To integrate calibration models with microbial growth models for process analysis.

Main Methods:

  • A bottom-up approach to non-linear calibration modeling.
  • Application to optical biomass measurement and enzymatic glucose quantification.
  • Implementation in Python packages: calibr8 and murefi.
  • Integration with hierarchical Monod-like ordinary differential equation models.

Main Results:

  • Demonstrated improved accessibility of uncertainty quantification for calibration tasks.
  • Enabled more reproducible and automatable data analysis routines.
  • Successfully combined calibration models with microbial growth models for parameter learning.

Conclusions:

  • The developed framework and software packages enhance the understanding and implementation of non-linear calibration.
  • The approach facilitates more robust and reliable data analysis in high-throughput experimental sciences.
  • The methodology supports advanced applications in process modeling and machine learning.