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Record W2811315410 · doi:10.1002/ppp.1977

Climatic, geomorphologic and hydrologic perturbations as drivers for mid‐ to late Holocene development of ice‐wedge polygons in the western Canadian Arctic

2018· article· en· W2811315410 on OpenAlexfundaboutno aff
Juliane Wolter, Hugues Lantuit, Sebastian Wetterich, Janet Rethemeyer, Michael Fritz

Bibliographic record

VenuePermafrost and Periglacial Processes · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
FundersHelmholtz AssociationBundesministerium für Bildung und ForschungParks CanadaAurora Research Institute
KeywordsIce wedgePermafrostHoloceneGeologyArcticPhysical geographyClimate changeThermokarstGeomorphologyOceanographyGeography

Abstract

fetched live from OpenAlex

Ice-wedge polygons are widespread periglacial features and influence landscape hydrology and carbon storage. The influence of climate and topography on polygon development is not entirely clear, however, giving high uncertainties to projections of permafrost development. We studied the mid- to late Holocene development of modern ice-wedge polygon sites to explore drivers of change and reasons for long-term stability. We analyzed organic carbon, total nitrogen, stable carbon isotopes, grain size composition and plant macrofossils in six cores from three polygons. We found that all sites developed from aquatic to wetland conditions. In the mid-Holocene, shallow lakes and partly submerged ice-wedge polygons existed at the studied sites. An erosional hiatus of ca 5000 years followed, and ice-wedge polygons re-initiated within the last millennium. Ice-wedge melt and surface drying during the last century were linked to climatic warming. The influence of climate on ice-wedge polygon development was outweighed by geomorphology during most of the late Holocene. Recent warming, however, caused ice-wedge degradation at all sites. Our study showed that where waterlogged ground was maintained, low-centered polygons persisted for millennia. Ice-wedge melt and increased drainage through geomorphic disturbance, however, triggered conversion into high-centered polygons and may lead to self-enhancing degradation under continued warming.

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 score0.600
Threshold uncertainty score0.972

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 studies0.0010.000
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 head0.039
GPT teacher head0.266
Teacher spread0.227 · 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

Citations21
Published2018
Admission routes2
Has abstractyes

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