Suggestions(1)
Exact(3)
Model predictions depend on the parameters, some of which must be estimated from experimental data.
For the Rule Abstraction model, predictions depend on assumptions about how the task is controlled.
These discrepancies are at least in part because the model predictions depend on the age of the subject, yet the age was not specified for almost all the fasts reported in Tables 3 and 4 and hence we had to assume a fixed age (we used 35).
Similar(56)
The accuracy of model predictions depends on the constitutive models that relate capillary pressure (Pc) and relative permeability (kr) to phase saturations (Sw).
Significant non-linear response is observed in the model predictions depending on how the probability distributions of the uncertain rate constants are defined.
The CFD results show that the accuracy of model predictions depends on the proper evaluation of the frictional stress as well as on the values chosen for ew and φ.
The robustness of our model predictions depends in part on the relative importance of climate versus geographic and biotic limitations on species distributions.
The greater strength of rod → cone compared to cone → rod interactions in the LN model predictions depends directly on the rod linear filter being more biphasic than the cone filter; specifically, ∼200 ms after the flash, the overshoot of the rod filter is maximally effective at suppressing the ability of the cone-mediated response to traverse the nonlinearity.
The reliability of model prediction depends on (i) model concept, (ii) parameters, (iii) uncertainty of input data, and (iv) uncertainty of reference data.
As found in [ 14], the model predictions may depend on the numerical value of the threshold at which the rate of cell kill switches from low to high (in our model, this value is represented by the parameter K c ).
The main limit of the available models is that their predictions depend on a number of parameters which are usually adjusted to fit the experimental data obtained from laboratory deposition tests.
Write better and faster with AI suggestions while staying true to your unique style.
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