The search results were filtered with cross-correlation (Xcorr) via charge states (+1: 1.5, +2: 2.0, +4: 2.5, +4: 3.0), delta correlation (ΔCn > 0.1), molecular weight (m/z 500 Da m/z 2000 Da), and peptide mass accuracy (Δ1.5 Da) parameters.
Furthermore, the OPLS-DA parameters, RY and Q, were 0.819 and 0.74 for CK vs. NC, 0.856 and 0.595 for SK vs. NC, and 0.931 and 0.301 for CK vs. SK, respectively.
Furthermore, a contribution analysis found that the design parameter da and the climatic parameter WS were considered to obtain the most robust estimation model.
The parameter Da declined by over an order of magnitude from 4.15% for Model 3 to 0.231% for Model 4 and 0.189% for Model 5. Most of the variation among trees was accounted for by addition of a random term allowing for different intercepts in the longitudinal pattern (Model 4).
Temporal DA revealed nine parameters (water temperature, dissolved oxygen, total alkalinity, total hardness, nitrate nitrogen, ammonia nitrogen, total phosphorus, chloride and calcium), affording 100% correct assignations.
Spatial DA revealed eight parameters (water temperature, pH, dissolved oxygen, electrical conductivity, nitrate nitrogen, orthophosphate phosphorus, sodium and total suspended solids), affording 92.5% correct assignations.
Figure 2c also illustrates the impact of the lag and overshoot on the quantification of DA's kinetic parameters.
To produce a pairwise genetic (mtDNA) distance matrix, between group variation (corrected for within-group variation) was calculated using the net nucleotide distances (DA) (Kimura two-parameter model).
Unless extreme caution is used, it appears possible that these algorithms can accidently distort response features not caused by diffusion across a gap, including lag and overshoot, and thereby confound the quantification of DA's kinetic parameters.
