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This is crucial, as much of the artificiality associated with laboratory, and even mesocosm, approaches is removed by conducting manipulations in real habitats, which are characterised by natural environmental variability and complex interactions that often cannot be simulated under controlled conditions [12].
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If so, our subjects should perform better than chance at detecting manipulations in real-world scenes.
We had a number of predictions about people's ability to detect and locate manipulations in real-world photos.
We should note, however, that our study could have underestimated people's ability to locate manipulations in real-world photos.
On the one hand, we might expect people to perform even worse if trying to detect manipulations in real-world photos.
On the other hand, there is good reason to predict that people might do well at detecting manipulations in real-world scenes.
It is clear that people find it difficult to detect and locate manipulations in real-world photos, regardless of whether those manipulations lead to physically plausible or implausible scenes.
Most predictions on the consequences of global warming on marine biodiversity have been based either on climate envelope modeling or on inferences from highly artificial laboratory experiments, rather than from observations or manipulations conducted in real habitats.
In sum, if people find it particularly difficult to detect manipulations in complex real-world scenes, then we might expect our subjects to perform worse than Farid and Bravo's (2010) subjects.
Market manipulation, if real, would be making a bad situation only worse for consumers.
In [18], the authors propose several mechanisms for achieving this kind of manipulation security in real-life environments, especially targeting internal database mechanisms for providing rollbacks (so-called transaction logs 1).
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