ArticleRoyal Society open science2025
Meta-learning provides a robust framework to discern taxonomic carnivore agency from the analysis of tooth marks on bone: reassessing the role of felids as predators of
Article in Royal Society open science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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1 citing paper in PubMed.
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5 authors.
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Abstract
Determining carnivore agency in taphonomic research is crucial for identifying site formation processes and carnivore-hominin interactions that influenced human evolution. Previous deep learning (DL) models classified the four principal carnivore agents affecting African hominins, but exhibited uneven performance due to unbalanced sample sizes. This study introduces a dual method based on few-shot supervised learning (FSSL) and model-agnostic meta-learning (MAML) as an alternative, achieving more consistent accuracy (FSSL: 81.54-83.56%; MAML: 82.56-85.13%), and significantly improving macro-average F1 scores. The best performing MAML model, Xception, reached 85.13% accuracy and an 84% F1 score, with taxon-specific F1 scores of 82% (crocodiles), 83% (hyenas), 88% (leopards) and 83% (lions), making the most precise classification of carnivore-made tooth marks to date. Applying FSSL-MAML ensemble models to
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