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Causes of Decline of Yadgha Language/LES CAUSES DE DECLIN DE LANGUE DE YADGHA

2011· article· en· W7914224 on OpenAlexvenueno aff
Fauzia Janjua

Bibliographic record

VenueCanadian social science · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicSouth Asian Studies and Conflicts
Canadian institutionsnot available
Fundersnot available
KeywordsPrestigeIndigenousSociologyEthnologyLinguisticsVitalityPopulationIndigenous languageGeographyDemographyPhilosophy

Abstract

fetched live from OpenAlex

The object of this investigation was to determine whether glutamate uptake affects the apparent potency of the competitive antagonists DL-2-amino-5-phosphonovalerate and CGS-19755 in blocking NMDA receptor-mediated neurotoxicity. In astrocyte-rich rat cortical cultures we observed that DL-2-amino-5-phosphonovalerate and CGS-19755 were 24 and 16 times more potent against NMDA than against glutamate-induced toxicity. In contrast, DL-2-amino-5-phosphonovalerate was equipotent against the two agonists in astrocyte-poor cultures, in which dendrites are directly exposed to the extracellular medium. With the noncompetitive NMDA antagonist MK-801, similar potencies were observed against glutamate (212 +/- 16 nM) and against NMDA (155 +/- 9 nM) neurotoxicity. These results may be explained if we assume that the neuronal cell body is less susceptible than the dendrites to NMDA receptor-mediated toxicity, and that the action of glutamate in astrocyte-rich cultures is confined to the cell body. In this case, one would expect that higher concentrations of glutamate would be needed to produce toxicity in astrocyte-rich cultures, and that higher concentrations of competitive antagonists would be needed to overcome this toxicity. Our observations help explain the pharmacology of the competitive NMDA antagonists against NMDA receptor-mediated neurotoxicity but also suggest the possibility that, because the cell body and dendrites may be distinct sites for neurotoxicity, they might also involve different mechanisms of toxicity.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.148
Threshold uncertainty score0.766

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.047
GPT teacher head0.321
Teacher spread0.275 · 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

Citations1
Published2011
Admission routes1
Has abstractyes

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