Summary
Anthropogenic sites often emerge from overlapping actions and/or occupations that include disruptive processes. Consequently, the palimpsests that arise can have a disorderly appearance, which may complicate the interpretation of such sites. The problem is worsened when some agents, such as carnivore scavengers, act on the archaeological record, causing the remains to disappear and leaving no obvious signs of their passage. Such occurrences have important consequences for archaeological studies, especially when trying to apply ethnographically-derived postulates created in high-resolution temporal contexts. However, new interpretations that are more complete can overcome this problem if the behaviour of scavengers is modelled using controlled experimental programs with wild carnivores (neo-taphonomy). SCAVENGERS consists of monitoring experimental reproductions of hearth-related assemblages (like those described in archaeological contexts) exposed to different species of wild carnivores: hyenas, lions, bears, wolves, and other smaller carnivores. The results will then be subsequently tested on different Middle Palaeolithic assemblages using Artificial Intelligence through computer techniques based on convolutional neural networks. However, the main aim is to generate cross-sectional data that can be applied to all periods and geographic areas, overcoming the limits of traditional archaeological methods to make accurate inferences about past human behaviour. The results of this project will contribute to the development of a new paradigm, changing some basic ideas about prehistory. There is a risk of generating a huge and unmanageable quantity of variables from neo-taphonomy. However, accumulated information and experience will provide valuable data and information for other areas of knowledge, such as those related to naturalistic studies, animal behaviour, and management of natural resources/parks.
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| Web resources: | https://cordis.europa.eu/project/id/101097511 |
| Start date: | 01-01-2024 |
| End date: | 31-12-2028 |
| Total budget - Public funding: | 2 454 563,00 Euro - 2 454 563,00 Euro |
Cordis data
Original description
Anthropogenic sites often emerge from overlapping actions and/or occupations that include disruptive processes. Consequently, the palimpsests that arise can have a disorderly appearance, which may complicate the interpretation of such sites. The problem is worsened when some agents, such as carnivore scavengers, act on the archaeological record, causing the remains to disappear and leaving no obvious signs of their passage. Such occurrences have important consequences for archaeological studies, especially when trying to apply ethnographically-derived postulates created in high-resolution temporal contexts. However, new interpretations that are more complete can overcome this problem if the behaviour of scavengers is modelled using controlled experimental programs with wild carnivores (neo-taphonomy). SCAVENGERS consists of monitoring experimental reproductions of hearth-related assemblages (like those described in archaeological contexts) exposed to different species of wild carnivores: hyenas, lions, bears, wolves, and other smaller carnivores. The results will then be subsequently tested on different Middle Palaeolithic assemblages using Artificial Intelligence through computer techniques based on convolutional neural networks. However, the main aim is to generate cross-sectional data that can be applied to all periods and geographic areas, overcoming the limits of traditional archaeological methods to make accurate inferences about past human behaviour. The results of this project will contribute to the development of a new paradigm, changing some basic ideas about prehistory. There is a risk of generating a huge and unmanageable quantity of variables from neo-taphonomy. However, accumulated information and experience will provide valuable data and information for other areas of knowledge, such as those related to naturalistic studies, animal behaviour, and management of natural resources/parks.Status
SIGNEDCall topic
ERC-2022-ADGUpdate Date
31-07-2023
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