Translate the following paragraphs into Chinese. Determining whether our known fossil sites shared a distinctive spectral signature was no small task, because for each site we had to assess the combination of values in six bands of the electromagnetic spectrum provided by the Landsat data. Our problem was essentially one of pattern recognition in multiple dimensions, something that humans do not do particularly well but that computers excel at. So we enlisted a socalled artificial neural network—a computational model capable of learning complex patterns. Our artificial neural network revealed that the basin’s known fossil sites do indeed share a spectral signature, and it was able to easily tell these sandstone localities apart from other types of ground cover, such as wetlands and sand dunes. But the model had its limitations. Neural networks, by their very nature, are analytical “black boxes”, meaning they can distinguish patterns, but they do not reveal the actual factors that allow different patterns to be distinguished. So whereas our neural network could easily and accurately distinguish fossil localities from wetlands or sand dunes, it couldnot tell us how the spectral signatures of different land covers actually differed in the six bands of the Landsat data—information that could conceivably help us conduct a more targeted search. Another limitation of the neural network approach is that it is based entirely on the analysis of individual pixels. The problem is that the area of an individual Landsat pixel, which measures 225 square meters, does not necessarily correspond to the size of a fossil locality: Some localities are larger than an individual pixel; some are smaller. Thus, the neural network’s predictions about the location and extent of potential fossil sites (or a certain type of ground cover, for that matter) do not always match up with reality.