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Exact(5)
Similar to the OHLI, four possible answers were given for each omitted word.
The default values can be arbitrarily chosen (see below) and a warning is given for each omitted parameter definition or initial condition.
For respondents who had no reported intake of meat (n = 450), eggs (n = 1976), milk/dairy (n = 979) or vegetables (n = 11) and who had not reported organic consumption of the corresponding food group, we upscaled the sum index by multiplying with 6/5 for each omitted food category (additively).
For respondents who had no reported intake of meat (n=450), eggs (n=1976), milk/dairy (n=979) or vegetables (n=11) and who had not reported organic consumption of the corresponding food group, we upscaled the sum index by multiplying with 6/5 for each omitted food category.
However, for respondents who had no reported intake of meat (n = 361), eggs (n = 1462), milk/dairy (n = 718) or vegetables (7) and who had not reported organic consumption of the corresponding food group, we upscaled the sum index by multiplying with 6/5 for each omitted food category.
Similar(54)
This process is repeated for each study omitted in turn, and this allows the consistency of the developed model and its performance to be examined on multiple occasions.
To test phenotypic variation between genotypes, the same platform-specific models to be described for the SNP-FT association analyses were fitted for each platform omitting the SNP and population structure fixed effects.
We also ran reduced models for each response, omitting trial number and male relative mass as fixed effects (but including female identity as a random effect where appropriate), and compared their fit with the full models.
Once we had gathered all relevant full text papers we reviewed each reference list for any omitted studies.
For each assessment, we omitted scores on the nine misfitting items and ran the NAEP operational analysis to recompute the subgroup means (rounded and converted to the NAEP operational score scale) and the percentage of examinees at different proficiency levels.
Point values for each question were omitted to avoid any bias introduced by high- versus low-point-value questions.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com