Deep learning as a tool for ecology and evolution

ML Borowiec, RB Dikow, PB Frandsen… - Methods in Ecology …, 2022 - Wiley Online Library
Deep learning is driving recent advances behind many everyday technologies, including
speech and image recognition, natural language processing and autonomous driving. It is …

Taxonomic review of living planktonic foraminifera

GJA Brummer, M Kučera - Journal of Micropalaeontology, 2022 - jm.copernicus.org
Applications of fossil shells of planktonic foraminifera to decipher past environmental
change and plankton evolution require a robust operational taxonomy. In this respect, extant …

Using the fossil record to understand extinction risk and inform marine conservation in a changing world

S Finnegan, PG Harnik, R Lockwood… - Annual Review of …, 2024 - annualreviews.org
Understanding the long-term effects of ongoing global environmental change on marine
ecosystems requires a cross-disciplinary approach. Deep-time and recent fossil records can …

Quantifying the effect of anthropogenic climate change on calcifying plankton

L Fox, S Stukins, T Hill, CG Miller - Scientific Reports, 2020 - nature.com
Widely regarded as an imminent threat to our oceans, ocean acidification has been
documented in all oceanic basins. Projected changes in seawater chemistry will have …

[HTML][HTML] Zircon classification from cathodoluminescence images using deep learning

D Zheng, S Wu, C Ma, L Xiang, L Hou, A Chen… - Geoscience …, 2022 - Elsevier
Zircon is a widely-used heavy mineral in geochronological and geochemical research
because it can extract important information to understand the history and genesis of rocks …

Automated analysis of foraminifera fossil records by image classification using a convolutional neural network

R Marchant, M Tetard, A Pratiwi… - Journal of …, 2020 - jm.copernicus.org
Manual identification of foraminiferal morphospecies or morphotypes under stereo
microscopes is time consuming for micropalaeontologists and not possible for …

Automatic taxonomic identification based on the Fossil Image Dataset (> 415,000 images) and deep convolutional neural networks

X Liu, S Jiang, R Wu, W Shu, J Hou, Y Sun, J Sun… - Paleobiology, 2023 - cambridge.org
The rapid and accurate taxonomic identification of fossils is of great significance in
paleontology, biostratigraphy, and other fields. However, taxonomic identification is often …

Automatic identification of fossils and abiotic grains during carbonate microfacies analysis using deep convolutional neural networks

X Liu, H Song - Sedimentary Geology, 2020 - Elsevier
Petrographic analysis based on microfacies identification in thin sections is widely used in
sedimentary environment interpretation and paleoecological reconstruction. Fossil …

[HTML][HTML] Artificial intelligence in paleontology

C Yu, F Qin, A Watanabe, W Yao, Y Li, Z Qin, Y Liu… - Earth-Science …, 2024 - Elsevier
The accumulation of large datasets and increasing data availability have led to the
emergence of data-driven paleontological studies, which reveal an unprecedented picture of …

Species-level microfossil identification for globotruncana genus using hybrid deep learning algorithms from the scratch via a low-cost light microscope imaging

I Ozer, CK Ozer, AC Karaca, K Gorur, I Kocak… - Multimedia Tools and …, 2023 - Springer
Paleontologists generally use a low-cost electro-optical system to classify microfossils. This
manual identification is a time-consuming process and it may take about a long time …