Rebecca Estrada Aguila
Biographic Data
| ID | 147406 |
|---|---|
| NAME | Rebecca Estrada Aguila |
| GIVEN NAMES | Rebecca Estrada |
| FAMILY NAME | Aguila |
| SIGNATURE | AGUILA R E |
| AFFILIATIONS | Vanderbilt University |
| VERIFIED | No |
| TOTAL WORKS | 6 |
| TOTAL CITATIONS | 11 |
| AUTHOR COUNT | 6 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 2 |
Macrodebitage Writ Small? Studying Stoneknapping with Experimental Archaeology, Dynamic Image Analysis, and Statistics
Machine Learning–Based Identification of Lithic Microdebitage
Archaeologists tend to produce slow data that is contextually rich but often difficult to generalize. An example is the analysis of lithic microdebitage, or knapping debris, that is smaller than 6.3 mm (0.25 in.). So far, scholars have relied on manual approaches that are prone to intra- and interobserver errors. In the following, we present a machine learning–based alternative together with experimental archaeology and dynamic image analysis. We…
Redefining lithic microdebitage with experimental archaeology
Using tiny artifacts to answer big questions
The spatial analysis of microdebitage (measuring less than 6.3 mm) can identify areas where stone tools were knapped at archaeological sites. These tiny artifacts tend to become embedded in the locations where they were first deposited and are less vulnerable to post-depositional movement, making microdebitage an important artifact class for identifying primary areas of stone tool production. Traditional microdebitage analysis, however, can take …
Studying lithic microdebitage with a dynamic image particle analyzer
Lithic microdebitage has great archaeological potential to elucidate ancient stone tool production. So far, archaeologists have collected soil samples, separated them into size fractions, and analyzed them manually under a microscope to identify microdebitage. This time- and labor-intensive process has limited the number of samples and introduced intra- and inter-observer errors. Here, we discuss lithic microdebitage analysis with a dynamic image…
Using Dynamic Image Analysis as a Method for Discerning Microdebitage from Natural Soils in Archaeological Soil Samples
Unlike larger stone tools and debitage, the analysis of microdebitage (measuring less than 6 mm) allows for identifying likely areas where stone tools were manufactured at prehistoric archaeological sites. The tedious, time-consuming, and costly nature of microdebitage, however, has overshadowed its data potential, making most archaeologists wary of implementing this method. To alleviate these issues, this study introduces an experimental protoco…
Studying lithic microdebitage with a dynamic image particle analyzer
Lithic microdebitage has great archaeological potential to elucidate ancient stone tool production. So far, archaeologists have collected soil samples, separated them into size fractions, and analyzed them manually under a microscope to identify microdebitage. This time- and labor-intensive process has limited the number of samples and introduced intra- and inter-observer errors. Here, we discuss lithic microdebitage analysis with a dynamic image…
Machine Learning–Based Identification of Lithic Microdebitage
Archaeologists tend to produce slow data that is contextually rich but often difficult to generalize. An example is the analysis of lithic microdebitage, or knapping debris, that is smaller than 6.3 mm (0.25 in.). So far, scholars have relied on manual approaches that are prone to intra- and interobserver errors. In the following, we present a machine learning–based alternative together with experimental archaeology and dynamic image analysis. We…
Using Dynamic Image Analysis as a Method for Discerning Microdebitage from Natural Soils in Archaeological Soil Samples
Unlike larger stone tools and debitage, the analysis of microdebitage (measuring less than 6 mm) allows for identifying likely areas where stone tools were manufactured at prehistoric archaeological sites. The tedious, time-consuming, and costly nature of microdebitage, however, has overshadowed its data potential, making most archaeologists wary of implementing this method. To alleviate these issues, this study introduces an experimental protoco…
Redefining lithic microdebitage with experimental archaeology
Using tiny artifacts to answer big questions
The spatial analysis of microdebitage (measuring less than 6.3 mm) can identify areas where stone tools were knapped at archaeological sites. These tiny artifacts tend to become embedded in the locations where they were first deposited and are less vulnerable to post-depositional movement, making microdebitage an important artifact class for identifying primary areas of stone tool production. Traditional microdebitage analysis, however, can take …
Using Dynamic Image Analysis as a Method for Discerning Microdebitage from Natural Soils in Archaeological Soil Samples
Unlike larger stone tools and debitage, the analysis of microdebitage (measuring less than 6 mm) allows for identifying likely areas where stone tools were manufactured at prehistoric archaeological sites. The tedious, time-consuming, and costly nature of microdebitage, however, has overshadowed its data potential, making most archaeologists wary of implementing this method. To alleviate these issues, this study introduces an experimental protoco…
Using tiny artifacts to answer big questions
The spatial analysis of microdebitage (measuring less than 6.3 mm) can identify areas where stone tools were knapped at archaeological sites. These tiny artifacts tend to become embedded in the locations where they were first deposited and are less vulnerable to post-depositional movement, making microdebitage an important artifact class for identifying primary areas of stone tool production. Traditional microdebitage analysis, however, can take …
Studying lithic microdebitage with a dynamic image particle analyzer
Lithic microdebitage has great archaeological potential to elucidate ancient stone tool production. So far, archaeologists have collected soil samples, separated them into size fractions, and analyzed them manually under a microscope to identify microdebitage. This time- and labor-intensive process has limited the number of samples and introduced intra- and inter-observer errors. Here, we discuss lithic microdebitage analysis with a dynamic image…
Machine Learning–Based Identification of Lithic Microdebitage
Archaeologists tend to produce slow data that is contextually rich but often difficult to generalize. An example is the analysis of lithic microdebitage, or knapping debris, that is smaller than 6.3 mm (0.25 in.). So far, scholars have relied on manual approaches that are prone to intra- and interobserver errors. In the following, we present a machine learning–based alternative together with experimental archaeology and dynamic image analysis. We…
Redefining lithic microdebitage with experimental archaeology
Macrodebitage Writ Small? Studying Stoneknapping with Experimental Archaeology, Dynamic Image Analysis, and Statistics
Archaeology (6 works) · Image Processing and 3D Reconstruction (6 works) · Computer Science (5 works) · Geography (5 works) · Geology (4 works) · Pleistocene-Era Hominins and Archaeology (4 works) · Archaeology and ancient environmental studies (3 works) · Archaeological Research and Protection (2 works) · Artificial Intelligence (2 works) · History (2 works)