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Record W2177360948 · doi:10.1002/ppp.1870

Climate and Terrain Characteristics Linked to Mud Ejection Occurrence in the Canadian High Arctic

2015· article· en· W2177360948 on OpenAlexafffundabout
Jean Holloway, Scott F. Lamoureux, Scott Montross, Melissa J. Lafrenière

Bibliographic record

VenuePermafrost and Periglacial Processes · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsQueen's UniversityUniversity of Ottawa
FundersNatural Resources CanadaBayer CanadaNatural Sciences and Engineering Research Council of CanadaCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorArcticNet
KeywordsPermafrostArcticGeologyWatershedSedimentOceanographyPhysical geographyHydrology (agriculture)GeomorphologyGeography

Abstract

fetched live from OpenAlex

Abstract Pressurised slurries of fine‐grained sediment expelled from the base of the active layer have been observed in recent years in the High Arctic. Such mud ejections , however, are poorly understood in terms of how exactly climate and landscape factors determine when and where they occur. Mud ejections at the Cape Bounty Arctic Watershed Observatory, Melville Island, Nunavut, were systematically mapped in 2012 and 2013, and this was combined with observations of mud ejection activity and climatic measurements carried out since 2003. The mud ejections occur late in the melt season during warm years and closely following major rainfall events. High‐resolution satellite imagery demonstrates that mud ejections are associated with polar semi‐desert vegetative settings, flat or low‐sloping terrain and south‐facing slopes. The localised occurrence of mud ejections appears to be related to differential soil moisture retention. Copyright © 2015 John Wiley & Sons, Ltd.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.655
Threshold uncertainty score0.700

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)0.0000.000

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 head0.042
GPT teacher head0.260
Teacher spread0.218 · 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

Citations14
Published2015
Admission routes3
Has abstractyes

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