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“…washing, sieving and sorting) remains a bottleneck. Moreover, foraminifera stuck in hardened consolidated sediment are difficult to obtain and classify in this manner 16 , 18 . Fig.…”
Section: Background and Summarymentioning
confidence: 99%
“…Such methods involve taking photographs of sieved and washed foraminifera and using machine learning to subsequently classify the imaged foraminifera. This is done by defining hand-crafted features 14 or by learning the relevant features for classification based on the whole image of individual foraminifera [15][16][17] . Several efforts have incorporated 3D features by photographing specimens under different lighting conditions 14 , at different focal planes 18 or both 17 .…”
This paper introduces ForametCeTera, a pioneering dataset designed to address the challenges associated with automating the analysis of benthic foraminifera in sediment cores. Foraminifera are sensitive sentinels of environmental change and are a crucial component of carbonate-denominated ecosystems, such as coral reefs. Studying their prevalence and characteristics is imperative in understanding climate change. However, analysis of foraminifera contained in core samples currently requires washing, sieving and manual quantification. These methods are thus time-consuming and require trained experts. To overcome these limitations, we propose an alternative workflow utilizing 3D X-ray computational tomography (CT) for fully automated analysis, saving time and resources. Despite recent advancements in automation, a crucial lack of methods persists for segmenting and classifying individual foraminifera from 3D scans. In response, we present ForametCeTera, a diverse dataset featuring 436 3D CT scans of individual foraminifera and non-foraminiferan material following a high-throughput scanning workflow. ForametCeTera serves as a foundational resource for generating synthetic digital core samples, facilitating the development of segmentation and classification methods of entire core sample CT scans.
“…washing, sieving and sorting) remains a bottleneck. Moreover, foraminifera stuck in hardened consolidated sediment are difficult to obtain and classify in this manner 16 , 18 . Fig.…”
Section: Background and Summarymentioning
confidence: 99%
“…Such methods involve taking photographs of sieved and washed foraminifera and using machine learning to subsequently classify the imaged foraminifera. This is done by defining hand-crafted features 14 or by learning the relevant features for classification based on the whole image of individual foraminifera [15][16][17] . Several efforts have incorporated 3D features by photographing specimens under different lighting conditions 14 , at different focal planes 18 or both 17 .…”
This paper introduces ForametCeTera, a pioneering dataset designed to address the challenges associated with automating the analysis of benthic foraminifera in sediment cores. Foraminifera are sensitive sentinels of environmental change and are a crucial component of carbonate-denominated ecosystems, such as coral reefs. Studying their prevalence and characteristics is imperative in understanding climate change. However, analysis of foraminifera contained in core samples currently requires washing, sieving and manual quantification. These methods are thus time-consuming and require trained experts. To overcome these limitations, we propose an alternative workflow utilizing 3D X-ray computational tomography (CT) for fully automated analysis, saving time and resources. Despite recent advancements in automation, a crucial lack of methods persists for segmenting and classifying individual foraminifera from 3D scans. In response, we present ForametCeTera, a diverse dataset featuring 436 3D CT scans of individual foraminifera and non-foraminiferan material following a high-throughput scanning workflow. ForametCeTera serves as a foundational resource for generating synthetic digital core samples, facilitating the development of segmentation and classification methods of entire core sample CT scans.
“…Advances in geochemistry provide new insight into the evolution of dietary niches [ 18 , 19 , 20 , 21 ] and life history [ 22 ], not to mention the ability to geologically date fossils [ 23 ]. As well, of course, advances in artificial intelligence and machine learning have forever changed taphonomy [ 24 , 25 ], approaches to fieldwork [ 26 , 27 ], and trait analysis [ 28 , 29 , 30 ].…”
Advances in genetics and developmental biology are revealing the relationship between genotype and dental phenotype (G:P), providing new approaches for how paleontologists assess dental variation in the fossil record. Our aim was to understand how the method of trait definition influences the ability to reconstruct phylogenetic relationships and evolutionary history in the Cercopithecidae, the Linnaean Family of monkeys currently living in Africa and Asia. We compared the two-dimensional assessment of molar size (calculated as the mesiodistal length of the crown multiplied by the buccolingual breadth) to a trait that reflects developmental influences on molar development (the inhibitory cascade, IC) and two traits that reflect the genetic architecture of postcanine tooth size variation (defined through quantitative genetic analyses: MMC and PMM). All traits were significantly influenced by the additive effects of genes and had similarly high heritability estimates. The proportion of covariate effects was greater for two-dimensional size compared to the G:P-defined traits. IC and MMC both showed evidence of selection, suggesting that they result from the same genetic architecture. When compared to the fossil record, Ancestral State Reconstruction using extant taxa consistently underestimated MMC and PMM values, highlighting the necessity of fossil data for understanding evolutionary patterns in these traits. Given that G:P-defined dental traits may provide insight to biological mechanisms that reach far beyond the dentition, this new approach to fossil morphology has the potential to open an entirely new window onto extinct paleobiologies. Without the fossil record, we would not be able to grasp the full range of variation in those biological mechanisms that have existed throughout evolution.
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