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Holocene freshwater carbonate structures in the hyper-arid Gebel Uweinat region of the Sahara Desert (Southwestern Egypt)

2013· article· en· W1974503789 on OpenAlexfundno aff
Margarita Marinova, Anna Nele Meckler, Christopher P. McKay

Bibliographic record

VenueJournal of African Earth Sciences · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Space AgencyDeutsche ForschungsgemeinschaftNational Aeronautics and Space Administration
KeywordsGeologyCarbonateHoloceneAridShoreVegetation (pathology)PaleontologyPhysical geographyOceanographyGeography

Abstract

fetched live from OpenAlex

The eastern part of the Sahara is at present the driest region of the desert. Yet the extensive animal rock art in the area, presumed to depict real activities in the lives of the painters, suggests that environmental conditions were significantly different when the rock art was produced. Here we report on exploration of the area, which led to the discovery of morphologically-distinct carbonate structures that line the walls of two valleys in Gebel Uweinat, and were likely formed in standing water. The carbonate structures comprise what appear to be shoreline carbonate formations, and date back to 8100 and 9400 years BP. The chemical and morphological similarity of these formations to carbonate structures from modern lakes suggests that these lakes contained fresh, standing water suitable for human and animal use. However, the significant quartz content suggests that windblown sand was pervasive, and thus the vegetation cover may have been sparse. This discovery supports the possibility of grasslands in the area, which may have been able to support human habitation, and adds to the evidence for a wetter climate in the area in the early Holocene.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.025
GPT teacher head0.232
Teacher spread0.207 · 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 source (direct Gemma or distilled Codex), 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

Citations37
Published2013
Admission routes1
Has abstractyes

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