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Record W4413365719 · doi:10.1371/journal.pone.0329084

Morphodynamic Foundations of Sumer

2025· article· en· W4413365719 on OpenAlexfundno aff
Liviu Giosan, Reed Goodman

Bibliographic record

VenuePLoS ONE · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicMaritime and Coastal Archaeology
Canadian institutionsnot available
FundersDivision of Behavioral and Cognitive SciencesDivision of Ocean SciencesYork UniversityNational Science Foundation
KeywordsUrbanizationBeach morphodynamicsContext (archaeology)GeographyEcologyEnvironmental resource managementGeologyArchaeologyEnvironmental scienceBiologyPaleontology

Abstract

fetched live from OpenAlex

Economic mechanisms behind the emergence of ancient Sumer remain unavoidably speculative and should benefit from a better understanding of their environmental context. Abundance sustaining increased social complexity during the Uruk period (c. 6,000-5,200 y BP) has been traditionally ascribed to pastoralism, trade, and/or resource diversity. However, contemporary agricultural surpluses are hard to explain before adoption of large-scale irrigation systems. Here we use high-resolution satellite-based topography and paleoenvironmental proxies from a new drill core at Lagash/Tell Al Hiba, together with previous geological and archaeological data, to reconstruct the morphodynamic evolution of coastal Sumer. We propose that tidal irrigation offers a plausible jumpstarting mechanism for high-yield, diversified agriculture providing an impetus for urbanization. As access to sea was restricted by delta build-up and tides shifted with the advancing deltaic coast, intensified reliance on mercurial river regimes eventually led to the expansive fluvial irrigation network of Early Dynastic city-states. By positioning coastal morphodynamics as a pivotal factor in urbanization and political ecology, we underscore the intricate interconnections between naturally evolving systems and collective human agency.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.488
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0050.000

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 head0.047
GPT teacher head0.223
Teacher spread0.176 · 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 designTheoretical or conceptual
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

Citations2
Published2025
Admission routes1
Has abstractyes

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