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Record W2969412480 · doi:10.1017/qua.2019.42

Neolithic population crash in northwest Europe associated with agricultural crisis

2019· article· en· W2969412480 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueQuaternary Research · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsTrent University
Fundersnot available
KeywordsAgriculturePopulationProductivityGeographyPopulation densityPopulation declinePhysical geographyClimate changeAgricultural productivityCropArchaeologyDemographyEcologyBiologyForestry

Abstract

fetched live from OpenAlex

Abstract The focus of this paper is the Neolithic of northwest Europe, where a rapid growth in population between ~5950 and ~5550 cal yr BP is followed by a decline that lasted until ~4950 cal yr BP. The timing of the increase in population density correlates with the local appearance of farming and is attributed to the advantageous effects of agriculture. However, the subsequent population decline has yet to be satisfactorily explained. One possible explanation is the reduction in yields in Neolithic cereal-based agriculture due to worsening climatic conditions. The suggestion of a correlation between Neolithic climate deterioration, agricultural productivity, and a decrease in population requires testing for northwestern Europe. Data for our analyses were collected during the Cultural Evolution of Neolithic Europe project. We assess the correlation between agricultural productivity and population densities in the Neolithic of northwest Europe by examining the changing frequencies of crop and weed taxa before, during and after the population “boom and bust.” We show that the period of population decline is coincidental with a decrease in cereal production linked to a shift towards less fertile soils.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.028
GPT teacher head0.259
Teacher spread0.231 · 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