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Woody species diversity, structure and community distribution along environmental gradients of Seqela Dry Afromontane forest in Northwestern Ethiopia.
PLoS One
January 2025
School of Geography, Geology and the Environment, Institute for Environmental Futures, University of Leicester, Leicester, United Kingdom.
Dry evergreen Afromontane forests are severely threatened due to the expansion of agriculture and overgrazing by livestock. The objective of this study was to investigate the composition of woody species, structure, regeneration status and plant communities in Seqela forest, as well as the relationship between plant community types and environmental variables. Systematic sampling was used to collect vegetation and environmental data from 52 (20 m x 20 m) (400 m2) plots.
View Article and Find Full Text PDFDevelopment of national post-fire restoration system to assess net GHG impacts and salvage biomass availability.
MethodsX
December 2024
Natural Resources Canada, Canadian Forest Service, 506 Burnside Road West, Victoria, BC, V8Z1M5, Canada.
In light of the recent unprecedented wildfires in Canada and the potential for increasing burned areas in the future, there is a need to explore post-fire salvage harvest and restoration and the implications for greenhouse gas (GHG) emissions. Salvage logging and replanting initiatives offer a potential solution by regrowing forests more quickly while meeting societal demands for wood and bioenergy. This study presents a comprehensive modeling framework to estimate post-fire salvage biomass and net GHG emissions relative to a 'do-nothing' baseline for all of Canada's harvest-eligible forests.
View Article and Find Full Text PDFChatGPT-Assisted Machine Learning for Chronic Disease Classification and Prediction: A Developmental and Validation Study.
Cureus
December 2024
Department of Medicine, Medical Teaching Institution (MTI) Hayatabad Medical Complex, Peshawar, PAK.
Background Chronic diseases such as chronic kidney disease (CKD), chronic liver disease (CLD), tuberculosis (TB), dementia, and heart disease are global health concerns of significant importance, representing major causes of morbidity and mortality worldwide. Early diagnosis and interventions are critical to improve patient outcomes and reduce healthcare costs. Methods This prospective observational study analyzed clinical data from 270 patients (calculated using G*Power 3.
View Article and Find Full Text PDFThe Role of Machine Learning Approaches in Pediatric Oncology: A Systematic Review.
Cureus
January 2025
General Practice, Wad Medani Hospital, Wad Medani, SDN.
To enhance patient outcomes in pediatric cancer, a better understanding of the medical and biological risk variables is required. With the growing amount of data accessible to research in pediatric cancer, machine learning (ML) is a form of algorithmic inference from sophisticated statistical techniques. In addition to highlighting developments and prospects in the field, the objective of this systematic study was to methodically describe the state of ML in pediatric oncology.
View Article and Find Full Text PDFLarge-scale and long-term wildlife research and monitoring using camera traps: a continental synthesis.
Biol Rev Camb Philos Soc
January 2025
Wildlife Observatory of Australia (WildObs), Queensland Cyber Infrastructure Foundation (QCIF), Brisbane, Queensland, 4072, Australia.
Camera traps are widely used in wildlife research and monitoring, so it is imperative to understand their strengths, limitations, and potential for increasing impact. We investigated a decade of use of wildlife cameras (2012-2022) with a case study on Australian terrestrial vertebrates using a multifaceted approach. We (i) synthesised information from a literature review; (ii) conducted an online questionnaire of 132 professionals; (iii) hosted an in-person workshop of 28 leading experts representing academia, non-governmental organisations (NGOs), and government; and (iv) mapped camera trap usage based on all sources.
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