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The low estimation accuracy earlier in the growing season could be due to model sensitivity to non-homogenous canopy architecture and soil background interference before full canopy closure.
The "unexplained" differences, that is, the residual differences in income or labor force participation or occupational attainment are then often attributed to discrimination but may in reality be due to model misspecification, omitted variables, and/or unobservable aspects of productivity differences.
This difference may be due to model HH training.
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Studies find that major uncertainties are due to model inputs, e.g., emissions and meteorology, more so than the model itself.
Hence, the bias in the estimates of the prediction error observed in the inner loop, which is due to model selection, is absent.
Uncertainties caused by model structure are due to model assumptions and simplifications because of insufficient knowledge, to limit data requirements and for operational reasons (e.g. application at a scale that requires model simplification).
Any differences between these 200 models is due to model uncertainty created by sampling.
If not, the death was classified as being due to model failure.
Including more variables into the model increases the apparent c-index to 0.79 0.80, but a substantial part of the apparent increase of predictability is due to model optimism.
The slightly larger deviations between calculated and experimental C NMR chemical shifts for the E. coli O101 polysaccharide compared to the Klebsiella pneumoniae polysaccharide are due to model approximations and available oligosaccharides in the current database.
This is due to models' less ability in capturing the randomly new developed areas.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com