Jordy Didier Orellana Figueroa
Biographic Data
| ID | 6915440 |
|---|---|
| NAME | Jordy Didier Orellana Figueroa |
| GIVEN NAMES | Jordy Didier Orellana |
| FAMILY NAME | Figueroa |
| SIGNATURE | FIGUEROA J D O |
| AFFILIATIONS | University of Tübingen |
| ORCID | 0000-0002-2814-6306 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 1 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 1 |
Machine Learning Applications in Archaeological Practices
Artificial intelligence and machine learning applications in archaeology have increased significantly in recent years, and these now span all subfields, geographical regions, and time periods: from prehistory to modern times. The prevalence and success of these applications have remained largely unexamined, as recent reviews on the use of machine learning in archaeology have only focused only on specific subfields of archaeology. Our review exami…
Trade-Offs in Standardizing Raw Materials
Experimental stone tool replication is an important method for understanding the context and production of prehistoric technologies. Experimental control is valuable for restricting the influence of confounding variables. Researchers can exert control in studies related to cognition and behavior by standardizing the type, form, and size of raw materials. Although standardization measures are already part of archaeological practice, specific proto…
Trade-Offs in Standardizing Raw Materials
Experimental stone tool replication is an important method for understanding the context and production of prehistoric technologies. Experimental control is valuable for restricting the influence of confounding variables. Researchers can exert control in studies related to cognition and behavior by standardizing the type, form, and size of raw materials. Although standardization measures are already part of archaeological practice, specific proto…
Trade-Offs in Standardizing Raw Materials
Experimental stone tool replication is an important method for understanding the context and production of prehistoric technologies. Experimental control is valuable for restricting the influence of confounding variables. Researchers can exert control in studies related to cognition and behavior by standardizing the type, form, and size of raw materials. Although standardization measures are already part of archaeological practice, specific proto…
Machine Learning Applications in Archaeological Practices
Artificial intelligence and machine learning applications in archaeology have increased significantly in recent years, and these now span all subfields, geographical regions, and time periods: from prehistory to modern times. The prevalence and success of these applications have remained largely unexamined, as recent reviews on the use of machine learning in archaeology have only focused only on specific subfields of archaeology. Our review exami…
Archaeological Research and Protection (1 work) · Archaeology (1 work) · Archaeology and ancient environmental studies (1 work) · Artificial neural network (1 work) · Chemistry (1 work) · Cluster analysis (1 work) · History (1 work) · History (1 work) · Image Processing and 3D Reconstruction (1 work) · Knapping (1 work)