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Text-mined fossil biodiversity dynamics using machine learning.

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Summary
This summary is machine-generated.

Researchers developed an automated pipeline using machine learning to extract fossil occurrence data from scientific literature. This method efficiently compiles biodiversity data, aiding the study of evolutionary dynamics.

Keywords:
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Area of Science:

  • Paleontology and Computational Linguistics
  • Biodiversity Informatics

Background:

  • Understanding Earth's biodiversity history relies on fossil occurrence data, but compiling this information from literature is time-consuming.
  • Vast amounts of understudied fossil taxa exist in the paleontological record.
  • Manual data compilation presents a significant impediment to a comprehensive understanding of past life.

Purpose of the Study:

  • To develop and evaluate a machine-learning pipeline for automated extraction of fossil occurrence and age data from unstructured text.
  • To demonstrate the pipeline's efficacy using Bryozoa, a marine invertebrate group, as a case study.
  • To reduce the labor-intensive nature of compiling large paleontological datasets.

Main Methods:

  • Utilized natural language processing and supervised machine learning to recognize taxonomic names and geologic time intervals in scientific literature.
  • Developed a pipeline to automatically determine fossil species occurrences within specific age intervals from digitized texts.
  • Compared machine-generated error rates with human error rates in a trial setting.

Main Results:

  • The automated pipeline successfully extracted fossil occurrence and age data from paleontological literature.
  • Intermediate machine error rates were found to be comparable to human error rates.
  • Generated genus richness curves accurately reflected key patterns from established bryozoan diversity studies.

Conclusions:

  • The developed automated pipeline significantly reduces the time and effort required for compiling fossil occurrence datasets.
  • This computational approach offers a scalable solution for data extraction across various fossil taxa.
  • The methodology has the potential to accelerate research into macroevolutionary patterns and biodiversity dynamics.