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Article summary:

1. This study used the DS algorithm to calibrate the Gaofen-5 (GF-5) hyperspectral image and CWT to eliminate noise.

2. The Boruta algorithm was used to select characteristic bands of GF-5 for estimating heavy metals concentrations.

3. The RF, ELM, SVM, and BPNN algorithms were used to predict soil heavy metals concentrations with good accuracy.

Article analysis:

The article is generally reliable and trustworthy in its presentation of the research conducted on retrieving soil heavy metals concentrations based on GaoFen-5 hyperspectral satellite image at an opencast coal mine in Inner Mongolia, China. The article provides a detailed description of the methods used in the research as well as a comprehensive discussion of the results obtained from the experiments conducted. Furthermore, it also provides evidence for its claims by citing relevant studies and providing data from laboratory measurements and field observations.

However, there are some potential biases that should be noted in this article. Firstly, it does not provide any counterarguments or alternative perspectives on the research conducted or its results which could have provided a more balanced view of the topic discussed. Secondly, it does not discuss any possible risks associated with using satellite imagery for predicting soil heavy metal concentrations which could be important for understanding the implications of such research. Finally, it does not provide any information about how accurate or precise these predictions are which could be useful for assessing their reliability and usefulness in practical applications.

In conclusion, this article is generally reliable and trustworthy but there are some potential biases that should be noted when evaluating its content.