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For instance, they were asked if they thought the data collected was reasonable, how well they felt the hypothetical group designed the experiment, and how well the group evaluated the model.
We evaluated the model by comparing simulated annual productivity and long-term population trends to field data.
Eventually, two case studies reflecting the current European DAM evaluated the model, aiming to confirm its robustness and reliability.
We evaluated the model by comparing measured and simulated leaf area and tested the model sensitivity to architectural data errors for the estimation of important interception parameters.
We evaluated the model against eddy-covariance (EC) measurements from established stations in the Alps, and analyzed the main limitations which affect the model performance in mountainous regions.
For each site, we evaluated the model for estimating discards from bottom trawl fishery associated with European minimum conservation reference size regulations, as well as discards caused by the low economic value of catches.
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For each experiment, we ran 5,000 training steps on the same sample of SEM images, and then evaluated the models.
We evaluated the models, which were trained using N=5,000 or N=20,000 training frames.
We also evaluated the model-fit warnings associated with successful models (271 of 669, or 40.5%, successful data sets had at least one warning), and we found that the majority (64%) of these concerned extrapolating more than three times below the lowest non-zero dose (median values were 6.4 for BMDL and 5.0 for BMD extrapolations).
We evaluated the models' robustness and representativeness in three ways.
Different evaluation methods were used to evaluate the model predictive capacity on CTC count kinetics.
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CEO of Professional Science Editing for Scientists @ prosciediting.com