DOI: 10.5061/dryad.zpc866tpq
title: AI and paleontology: Effects of vertebrate fossil sample size on machine learning image classification

description: With the growing application of artificial intelligence (AI) and machine
 learning (ML), great potential exists to leverage these technologies in
 paleontology. Relative to many other scientific fields, a challenge of ML
 applied to paleontology is small sample sizes, particularly for fossil
 vertebrates. Shark teeth, abundant in the fossil record, provide a model
 system to use ML across varying sample sizes. Here we use six classes
 (taxa) of Neogene shark teeth for taxonomic identification, including a
 curated dataset of 3150 images. Each class was evaluated using an 80%
 training and 20% validation split, with a separate, external test set of
 25 samples per class. Pretrained models perform well (accuracy &gt;
 90%), providing a strong baseline for classification. However, enabling
 fine-tuning of the ML model to identify fossil shark teeth improves
 performance considerably. Likewise, sample size per class also affects the
 accuracy of the models’ classifications. Smaller sample sizes (n = 50
 individuals per class) yielded a mean accuracy of 93.4%, but plateaued at
 ~99% between 200 and 500 images per class. Confidence likewise increases
 with larger samples, from 81.8% (n = 50 individuals per class) to
 &gt;90% (n = 300 to 500 individuals per class). Misidentifications
 followed consistent patterns, reflecting morphological similarities and/or
 poor preservation. Artificially increasing the training datasets using
 data augmentation improves the confidence of identifications. This
 research indicates that relatively small samples of vertebrate species
 (~50 to 500 individuals per class) can effectively train an ML model to
 identify these shark teeth with high levels of accuracy.
description: # AI and paleontology: Effects of vertebrate fossil sample size on machine
 learning image classification Dataset DOI:
 [10.5061/dryad.zpc866tpq](https://doi.org/10.5061/dryad.zpc866tpq) ##
 Description of the data and file structure A complete README file,
 formatted as a .pdf is included in the file 1 README 1.28.26.pdf. The
 other files are listed as follows: *
 3c_Optimal_pixel_density_ML_model_results.csv *
 5b_Database_optimal_perf_ML_results_WO_fine_tuning.csv *
 5c_Database_optimal_perf_ML_results_W_fine_tuning.csv *
 5d_Variance_dataset.csv * 6b_Data_Augmentation-FT_model__RG-HF.csv *
 9_SharkAI_R_code_2025_sections_356.R * 1_README_1.29.2026.pdf *
 2_Complete_study_image_data_set.pdf *
 3ab_Optimal_pixel_density_context_and_statistical_results.pdf *
 4_Original_Python-Tensorflow_files_including_model_code.pdf *
 5a_Optimal_performance_statistical_results.pdf *
 6a_Data_augmentation_statisical_results_.pdf *
 7_Misidentification_analysis.pdf *
 8_GBIF_plots_of_fossil_Lamnidae_and_Carcharhinidae.pdf ### Files and
 variables Files and variables are contained in the 1 README 1.28.26 file
 and other files in the submission above. ## Code/software Code/software
 are contained in the 1 README 1.28.26 file submission above. ## Access
 information Access information are contained in the 1 README 1.28.26 file
 submission above.