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The rise times for the best models were evaluated as the average of 20 repeats.
In order to evaluate the performance of our models on independent data, we first trained our models on 80% of data by ten-fold cross validation and later the performance of the best models were evaluated on remaining 20% independent data.
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When a constrained region causes a multicollinearity problem, contributions of each variable in the best model are evaluated by the approximate model derived from substituting correlations between explanatory variables into the best model.
The globularity of the best model was evaluated according to our recent work [27].
Within each class of RNA-models, the best model is evaluated by an Akaike Information criterion (AICc) test.
Best-fit models were evaluated using jModeltest.
To select the best model, all optimized models were evaluated using the Profile-3D program [ 54].
In order to find the best model with a reliable performance, constructed models were evaluated and compared in terms of classification accuracy and computational cost.
Multiple closed population models were evaluated, with Chao Mh ranking best in model performance and yielding a population estimate of 668 sharks with 95% confidence intervals ranging from 289 1720.
Models were evaluated using ERRATv2, and the best scoring value for each is reported.
We thus repeated the forward selection process, and at each iteration, alternative models were evaluated using 500 cross-validation simulations, and the best ten models were then re-evaluated at each iteration using 20,000 cross-validation simulations.
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