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Evaluating causes of error in landmark-based data collection using scanners.

Brian M Shearer1,2,3, Siobhán B Cooke3,4, Lauren B Halenar2,3,5

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Digital craniodental landmarking precision is not affected by scanner type. Interobserver error significantly exceeds intraobserver error, highlighting the importance of researcher experience and standardized training for consistent morphometric data.

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Area of Science:

  • Paleoanthropology
  • Comparative Anatomy
  • Geometric Morphometrics

Background:

  • Digital reconstruction of fossil and extant specimens is crucial for morphometric analysis.
  • Craniodental landmarks are essential for comparative studies in primatology and paleoanthropology.
  • Understanding landmarking error is vital for reliable digital data collection.

Purpose of the Study:

  • To evaluate the precision, accuracy, and repeatability of craniodental landmarking on digital models.
  • To assess the impact of different digital scanning methods (surface vs. microCT) on landmarking error.
  • To determine the influence of researcher experience and training on intra- and interobserver landmarking variability.

Main Methods:

  • Craniodental landmarks and semilandmark curves were applied to a macaque cranium digitally reconstructed using three surface scanners and one microCT scanner.
  • Nine researchers with varying expertise landmarked ten iterations of each scan (40 total) to quantify observer error.
  • Two researchers landmarked ten additional macaque specimens across seven species to differentiate observer variance from biological variance.

Main Results:

  • Scan type (surface vs. microCT) did not significantly affect intra- or interobserver landmarking error rates.
  • Interobserver error was substantially higher than intraobserver error across all participants and scan types.
  • Researcher experience in osteology and geometric morphometrics positively correlated with landmarking precision.

Conclusions:

  • Standardized training protocols are essential for minimizing interobserver error in digital morphometric studies.
  • While scan quality does not appear to be a major factor, consistent landmarking methodology is critical.
  • Investments in researcher training can improve data reliability, especially when pooling data from multiple individuals or specimens.