Sentence examples for quantified error from inspiring English sources

Below, average total fluorescence intensity of CFP-KIF1C and mCherry-Rab6A from similarly treated cells fixed in paraformaldehyde and quantified (error bars = SE, >100 cells/condition).

(C ) Percentage of hyphae (8 h.p.i .: unipolarity, bipolarity, and septum formation was quantified (error bars, s.e.m.; n = 3 independent experiments, >100 hyphae were counted for each strain per experiment; note that septum formation is given relative to the values of unipolar or bipolar hyphae set to 100%).

(E ) Percentage of hyphae (8 h.p.i .: unipolarity, bipolarity, and septum formation was quantified (error bars, s.e.m.; n = 3 independent experiments, >100 hyphae were counted per experiment; note that septum formation is given relative to the values of unipolar or bipolar hyphae set to 100%).

(F ) Percentage of hyphae (8 h.p.i .: unipolarity, bipolarity, and septum formation was quantified (error bars, s.e.m.; n = 3 independent experiments, >100 hyphae were counted per experiment; note that septum formation is given relative to the values of unipolar or bipolar hyphae set to 100%).

(H ) Percentage of hyphae (8 h.p.i .: unipolarity, bipolarity, and septum formation was quantified (error bars, s.e.m.; n = 3 independent experiments, >100 hyphae were counted per experiment; note that septum formation is given relative to the values of unipolar or bipolar hyphae set to 100%).

Challenges remain in quantifying error of remote sensing-based LFMC estimations and linking LFMC to fire behavior and risk.

The use of numerical simulations is advantageous because maps of flux from the temperature proxy method can be compared to known flux maps to quantify error.

Sensitivity analyses were performed to identify important model parameters and quantify error in model estimates caused by inaccurate input data used during deterministic simulations.

To quantify error rates across loci, 95 duplicate samples were run and genotypes were compared.

A method for quantifying error rates, sensitivity, and specificity of the entire testing algorithm is being developed.

Our goal was to assess and quantify error resulting from use of surrogate exposures and characterize the impact of different surrogate exposures on error.