Random forest algorithm yields accurate quantitative prediction models of benthic light at intertidal sites affected by toxic Lyngbya majuscula blooms-Reference-Cited by-同舟云学术

Random forest algorithm yields accurate quantitative prediction models of benthic light at intertidal sites affected by toxic Lyngbya majuscula blooms

Published:2012-09 Issue: Volume:19 Page:46-52
ISSN:1568-9883
Container-title:Harmful Algae
language:en
Short-container-title:Harmful Algae

Author:

Kehoe M.,O’ Brien K.,Grinham A.,Rissik D.,Ahern K.S.,Maxwell P.

Publisher

Elsevier BV

Subject

Plant Science,Aquatic Science

Reference23 articles.

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2. In situ field experiment shows Lyngbya majuscula (cyanobacterium) growth stimulated by added iron, phosphorus and nitrogen;Ahern;Harmful Algae,2008

3. Blooms of the cyanobacterium Lyngbya majuscula in coastal Queensland, Australia: disparate sites, common factors;Albert;Marine Pollution Bulletin,2005

4. Evaluation of the current state of mechanistic aquatic biogeochemical modeling: citation analysis and future perspectives;Arhonditsis;Environmental Science & Technology,2006

5. The exposure of green turtles (Chelonia mydas) to tumour promoting compounds produced by the cyanobacterium Lyngbya majuscula and their potential role in the aetiology of fibropapillomatosis;Arthur;Harmful Algae,2008

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