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Marine exploitation and the arrival of farming: resolving the paradox of the Mesolithic-Neolithic transition in Denmark

2025· article· en· W4410911416 on OpenAlexaff
Rowan McLaughlin, Harry K. Robson, Rikke Maring, Adam Boëthius, Eric Guiry, Daniel Groß, Satu Koivisto, Bente Philippsen, Nicky Milner, Geoff Bailey, Oliver E. Craig

Bibliographic record

VenueQuaternary Science Reviews · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsTrent University
Fundersnot available
KeywordsMesolithicGeographyArchaeologyPhysical geography

Abstract

fetched live from OpenAlex

The transition to farming in the coastal environments of southern Scandinavia remains a key conundrum in European prehistory. This region was heavily exploited by Late Mesolithic communities of the Ertebølle culture, complex hunter-fisher-gatherers who flourished for some 1500 years prior to the arrival of farming at around 4000 BCE marking the start of the Neolithic period. Extensive genetic and isotopic analyses of skeletal remains suggests that the arrival of farming is marked by a rapid demographic change and that incoming populations of ‘farmers’ had little reliance on marine resources. In contrast, frequent archaeological finds of shell middens and fishing gear in the Early Neolithic supports evidence for continuity in the use of marine resources across the transition. To assess this apparent paradox, focusing on the Danish evidence, we explore the spatiotemporal trends in the density of some 1500 radiocarbon dates using new informatics tools and modelling strategies. We indeed find strong archaeological indicators of sustained and even intensified patterns of coastal exploitation across and beyond the transition; shell middens, fishing implements, and aquatic residues in ceramics continue well into the Neolithic. Using an agent-based demographic model, we demonstrate how small differences in fertility could rapidly dilute signals of coastal resource use in the context of a growing Neolithic population. More broadly, we suggest that complex palimpsests of archaeological remains and biological information from human remains can only usefully be interpreted through the lens of demography.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.238
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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