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Last January, Dr. Reuben Granich and colleagues at the World Health Organization published a provocative study using mathematical models to predict the effects of universal testing and immediate treatment on a severe H.I.V. epidemic among heterosexuals.
The researchers used 19 different climate models to predict the effects of a 2C increase in global average temperatures, and found that by 2030 around 70% of the reefs would suffer what they politely call "long-term degradation".
We combined spatially-explicit growth models with wildlife distribution models to predict the effects of human development on 5 forest-dependent bird species in Vermont, New Hampshire, and Massachusetts, USA.
Background: The need to use animal models to predict the effects of drugs on the nervous system has led to the development of automated systems for the assessment of specific behaviors identical in both humans and laboratory animals that represent aspects of specific cognitive processes.
Factors such as driver-related, traffic-related, environment-related and geometric design-related were considered when developing statistical models to predict the effects of these factors on the severity of injuries sustained from motor vehicle crashes at merging and diverging locations.
Ewert et al. (2002) used crop simulation models to predict the effects of elevated CO2 concentration on wheat yield, using data from FACE experiments and open-top chamber experiments in Germany.
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Finally, we use the model to predict the effects of changes in the topology of the underlying protein-interaction network.
Researchers at the National Center for Atmospheric Research used a computer model to predict the effect of a future "grand solar minimum" on Earth's climate from 2020 to 2070.
Regression models were developed to predict the effects of variables on the studied indices.
The models are computationally executed to predict the effects of thousands of untested perturbations.
A causal model can be used to predict the effects of such an intervention.
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