Comparison and optimization of genetic tools used for the identification of ancient fish remains recovered from archaeological excavations and museum collections in the Mediterranean region
Bibliographic record
Abstract
Abstract Among the many fish species commercially exploited since prehistoric times, Atlantic bluefin tuna ( Thunnus thynnus ) is one of the most economically significant, having left an indelible imprint on several civilizations including the Phoenicians, Greeks, and Romans. Here, we describe our efforts to identify tuna specimens among the remains of 345 fish vertebrae and bones in several large collections from the Atlantic Ocean, Mediterranean Sea, and Black Sea, dating from the Late Iron Age (2nd century BCE) to the early 20th century (1911–1927). Unfortunately, ancient fish specimens are often mislabelled, which can cause a great deal of confusion among zoologists. Protocols were developed and optimized to overcome the unique challenges related to the compromised integrity of genetic material preserved in ancient bones. Three DNA isolation protocols were compared to maximize yields, and as reported for other faunal remains, a silica spin column‐based method was proven most effective. Endogenous DNA was successfully extracted from the majority of bones and amplified using polymerase chain reactions (PCRs) and an assortment of four primer pairs targeting nuclear (internal transcribed spacer) and mitochondrial sequences (cytochrome oxidase subunit 1 and control region). Protocols targeting mitochondrial markers were more successful than those focused on nuclear targets. Due to the restricted length of the extracted DNA molecules, character‐based keys containing diagnostic nucleotide substitutions were defined and used to identify 231 samples to genera, of which 171 were identified to species level. The success rate of assignment of specimens to species level varied between location and collection, reflecting variation in DNA preservation between different sites and environments. The methods detailed herein can be used to identify other ancient fish specimens and provide information about historical human diets, trade, species distribution, and biodiversity. The same tools can be applied to the analysis of processed food items with highly damaged DNA.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".