New insights into 4,000 years of resource economy across Greenland using ancient DNA
Bibliographic record
Abstract
Abstract The success and failure of past cultures across the Arctic was tightly coupled to the ability of past people to exploit the full range of resources available to them, and to adapt to fluctuations in resource availability. There is substantial evidence for the hunting of birds, caribou and a wide range of marine mammals in pre-historic Greenland from bone remains preserved in ancient middens. However, the extent to which these communities relied on marine resources such as fish and large cetaceans is understudied because of the taphonomic processes and bias that affect how these taxa present themselves in the archaeological record. To address this, we analyse DNA from bulk bone samples from 12 archaeological sites across Greenland dating from Paleo-Inuit to Neo-Inuit periods. Using a combination of metabarcoding and shotgun metagenomics we identify an assemblage of 43 species consisting of birds, fish, and both marine and terrestrial mammals. We find genetic evidence of five different whale species, of which the bowhead whale ( Balaena mysticetus ) was the most commonly detected. Furthermore, we detect nine fish species, of which four have not previously been identified in any of the studied sites. Lastly, we identify a novel haplotype in caribou ( Rangifer tarandus ) at the 3,000-year-old site Itinnera, suggesting the presence of a distinct lineage of (now extinct) dwarfed caribou that colonised Greenland after the last ice age 9,000 years ago. Collectively, these findings provide a rare insight into whaling and fishing practices in Greenland and demonstrate that prehistoric Greenlandic communities had the social and technological capacity to target the largest whales available in the waters around them.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".