Editors can now comment on articles published in PeerJ and elsewhere.
Rapid rise in toxic load for bees revealed by analysis of pesticide use in Great Britain (2018) PeerJ
Graciela Piñeiro –– This is an interesting and very important article and as such I am very glad to know that it was the most shared last month. It clearly demonstrates the high degree of th... Read more
Editor rating: 8 / 10Tal Svoray –– Comparison between machine learning tools and geostatistics are in the front of geosciences
Editor rating: 7 / 10Andrea Cucco –– The deep oil spill is still an open issue in operational oceanography and this job can be considered a further step forward in understanding this phenomenon.
Editor rating: 7 / 10Nicolas Rougier –– An important study about reproducibility in GIS whose methods could be re-used in other domains.
Editor rating: 7 / 10I. Emma Huertas –– The new dataset provided in an under explored region
Editor rating: 7 / 10Paolo Giordani –– New insights for understanding the processes of sorption and mineralization of PAH in soils
Editor rating: 7 / 10Young-Chae Song –– Dielectrophoresis is a novel approach to remove for ammonia nitrogen from water.
Editor rating: 8 / 10Bruno Marino –– The encroachment of human activity on natural systems is a complex interplay of events recorded through time and deposition of proxy indicators. "Who Killed Frame Lake.." is an iconic journey through time for the lake and its signatory participants. The future and remediation of Frame Lake is now in the hands of planners who may benefit from this example of human/nature interaction.
Editor rating: 9 / 10Hong Yang –– This paper increases our understanding of the water resource in oases in West China in the future climate change.
Editor rating: 8 / 10Samuel Abiven –– This work combines technical and methodological improvements and potential applications for agriculture. It could have an impact for different aspects on the scientific community.
Editor rating: 7 / 10Jacyra Soares –– The river air-water temperature relationship was investigated by developing three different machine learning models and three standard models. The model results can have important applications such as to support river recovery management plan and environmental impact statement analysis.
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