AIMC Topic: Fossils

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Improving micromorphological analysis with CNN-based segmentation of flint/obsidian, bone and charcoal.

PloS one
The quantification and identification of components in archaeological micromorphology remain subjective and challenging, particularly for early-career researchers. To address this, we developed a deep learning tool for the automatic segmentation of t...

Organic geochemical evidence for life in Archean rocks identified by pyrolysis-GC-MS and supervised machine learning.

Proceedings of the National Academy of Sciences of the United States of America
Throughout Earth's history, organic molecules from both abiogenic and biogenic sources have been buried in sedimentary rocks. Most of these organic molecules have been significantly altered by geologic processes through deep time. Nonetheless, the na...

Pollen morphology, deep learning, phylogenetics, and the evolution of environmental adaptations in Podocarpus.

The New phytologist
Podocarpus pollen morphology is shaped by both phylogenetic history and the environment. We analyzed the relationship between pollen traits quantified using deep learning and environmental factors within a comparative phylogenetic framework. We inves...

Deep learning-aided segmentation combined with finite element analysis reveals a more natural biomechanic of dinosaur fossil.

Scientific reports
Finite element analysis (FEA), a biomechanical simulation technique capable of providing direct mechanical visualization for CT-based digital models, has been extensively applied to fossil image datasets to address key evolutionary questions in paleo...

Enhancing the classification of isolated theropod teeth using machine learning: a comparative study.

PeerJ
Classifying objects, such as taxonomic identification of fossils based on morphometric variables, is a time-consuming process. This task is further complicated by intra-class variability, which makes it ideal for automation via machine learning (ML) ...

Capillariid diversity in archaeological material from the New and the Old World: clustering and artificial intelligence approaches.

Parasites & vectors
BACKGROUND: Capillariid nematode eggs have been reported in archaeological material in both the New and the Old World, mainly in Europe and South America. They have been found in various types of samples, as coprolites, sediments from latrines, pits,...

Making sense of fossils and artefacts: a review of best practices for the design of a successful workflow for machine learning-assisted citizen science projects.

PeerJ
Historically, the extensive involvement of citizen scientists in palaeontology and archaeology has resulted in many discoveries and insights. More recently, machine learning has emerged as a broadly applicable tool for analysing large datasets of fos...

Inferring the locomotor ecology of two of the oldest fossil squirrels: influence of operationalization, trait, body size and machine learning method.

Proceedings. Biological sciences
Correlations between morphology and lifestyle of extant taxa are useful for predicting lifestyles of extinct relatives. Here, we infer the locomotor behaviour of from the middle Oligocene and from the lower Miocene of France using their femoral mor...

Accelerating segmentation of fossil CT scans through Deep Learning.

Scientific reports
Recent developments in Deep Learning have opened the possibility for automated segmentation of large and highly detailed CT scan datasets of fossil material. However, previous methodologies have required large amounts of training data to reliably ext...

Trait-mediated speciation and human-driven extinctions in proboscideans revealed by unsupervised Bayesian neural networks.

Science advances
Species life-history traits, paleoenvironment, and biotic interactions likely influence speciation and extinction rates, affecting species richness over time. Birth-death models inferring the impact of these factors typically assume monotonic relatio...