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Record W31009077 · doi:10.1111/nph.15867

Dark Side of the Rock : Borders, Exceptionalism, and the Precarious Case of Ceuta and Melilla

2012· article· en· W31009077 on OpenAlexaff
Can E. Mutlu, Christopher C. Leite

Bibliographic record

VenueEurasia Border Review · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsUniversity of Ottawa
FundersBasic Energy SciencesOffice of Science
KeywordsSituatedExceptionalismContext (archaeology)Border crossingGeographySubject (documents)Economic geographyObject (grammar)ImmigrationPolitical scienceLawComputer scienceArchaeologyPolitics

Abstract

fetched live from OpenAlex

The capability to maintain cell wall integrity is critical for plants to adapt to unfavourable conditions. l-Arabinose (Ara) is a constituent of several cell wall polysaccharides and many cell wall-localised glycoproteins, but so far the contribution of Ara metabolism to abiotic stress tolerance is still poorly understood. Here, we report that mutations in the MUR4 (also known as HSR8) gene, which is required for the biosynthesis of UDP-Arap in Arabidopsis, led to reduced root elongation under high concentrations of NaCl, KCl, NaNO<sub>3</sub> , or KNO<sub>3</sub> . The short root phenotype of the mur4/hsr8 mutants under high salinity is rescued by exogenous Ara or gum arabic, a commercial product of arabinogalactan proteins (AGPs) from Acacia senegal. Mutation of the MUR4 gene led to abnormal cell-cell adhesion under salt stress. MUR4 forms either a homodimer or heterodimers with its isoforms. Analysis of the higher order mutants of MUR4 with its three paralogues, MURL, DUR, MEE25, reveals that the paralogues of MUR4 also contribute to the biosynthesis of UDP-Ara and are critical for root elongation. Taken together, our work revealed the importance of the Ara metabolism in salt stress tolerance and also provides new insights into the enzymes involved in the UDP-Ara biosynthesis in plants.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.927
Threshold uncertainty score0.412

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0000.000

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 head0.017
GPT teacher head0.352
Teacher spread0.335 · 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 designNot applicable
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

Citations9
Published2012
Admission routes1
Has abstractyes

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