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Record W4391822358 · doi:10.1007/s10814-024-09197-3

Long-Term Urban and Population Trends in the Southern Mesopotamian Floodplains

2024· article· en· W4391822358 on OpenAlexfundno aff
Nicolo' Marchetti, Eugenio Bortolini, Jessica C. Menghi Sartorio, Valentina Orrù, Federico Zaina

Bibliographic record

VenueJournal of Archaeological Research · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsnot available
FundersEuropean CommissionUniversity of TorontoUniversità di BolognaUniversity of ChicagoUniversity of OxfordVolkswagen Foundation
KeywordsFloodplainTerm (time)GeographyPopulationHistoryDemographyCartographySociology

Abstract

fetched live from OpenAlex

Abstract The processes of long-term urbanization in southern Mesopotamia are still insufficiently investigated, even though recent studies using large datasets and focusing on neighboring regions have paved the way to understanding the critical role of multiple variables in the shaping of settlement strategies by ancient human societies, among which climate change played an important role. In this paper, we tackle these issues by analyzing, within the new FloodPlains Web GIS project, a conspicuous amount of archaeological evidence collected over the past decades at approximately 5000 sites in southern Mesopotamia. We have measured modifications over time in a variety of demographic proxies generated through probabilistic approaches: our results show that the rapid climate changes, especially those that occurred around 5.2, 4.2, and 3.2 ka BP, may have contributed—in addition to other socioeconomic factors—to triggering the main urban and demographic cycles in southern Mesopotamia and that each cycle is characterized by specific settlement strategies in terms of the distribution and the dimension of the urban centers.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.674

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.061
GPT teacher head0.331
Teacher spread0.270 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations11
Published2024
Admission routes1
Has abstractyes

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