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Record W4400484423 · doi:10.1016/j.ecolind.2024.112287

An assessment of climate change impacts on oases in northern Africa

2024· article· en· W4400484423 on OpenAlex
Walter Leal Filho, Robert Stojanov, C. Matsoukas, Roberto Ingrosso, James Franke, Francesco S. R. Pausata, Tommaso Grassi, Jaromír Landa, Moulay Chérif Harrouni

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

VenueEcological Indicators · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil erosion and sediment transport
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsClimate changeEnvironmental sciencePrecipitationGlobal warmingEcosystemGeographyPhysical geographyEcologyClimatologyMeteorologyGeology

Abstract

fetched live from OpenAlex

• Oases are vulnerable ecosystems affected by climate change. • Projected air temperature changes under an extreme global warming scenario are statistically significant for all oases studied. • The impact of the projected changes is likely to lead to a greater groundwater demand. • Water shortage is a trend paralleled by a reduction in precipitation. Oases are vulnerable ecosystems that are affected by climate change. Using high-resolution climate models focusing on northern Africa, we investigate the changes in the agrosystems of oases. Projected air temperature changes under an extreme global warming scenario are statistically significant for all oases studied, with an increase of up to 4–4.5 °C by the end of the century. The impact of the projected warming is likely to lead to an increased groundwater demand, with a parallel trend in reduced precipitation. Combined, these processes endanger the long-term socio-economic prospect of oases.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.042
GPT teacher head0.310
Teacher spread0.268 · 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