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A landsystems approach to understanding the evolution of ice-cored topography and distribution of retrogressive thaw slumps, western Canadian Arctic

2020· article· en· W3093608506 on OpenAlexaffabout
Peter Morse, S A Wolfe, Steve V. Kokelj

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsGovernment of Northwest TerritoriesGeological Survey of CanadaNatural Resources Canada
Fundersnot available
KeywordsGeologyThermokarstGlacial periodPermafrostIce sheetQuaternaryPhysical geographyBedrockGeomorphologyArcticPaleontologyOceanographyGeography

Abstract

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The landscape of the Tuktoyaktuk Coastlands, western Canadian Arctic is dominated by glacial and geocryological processes that have modified, imprinted and sculpted the surface, depositing surficial materials upon underlying bedrock. Climate warming continues in this region at a rate that is twice the global average, and retrogressive thaw slump (RTS) activity is increasing. Recently, RTS distribution was associated with glacial limits reached by the Laurentide Ice Sheet and corresponding morainal deposits, but RTS are common in other local terrain units. In this glacial-marginal region, permafrost existed pre-glacially, and non-glacial geomorphic processes occurred throughout the Late Quaternary. Superimposed on these conditions are the effects of thermokarst during the Holocene climatic optimum, followed by a period of cooling. Collectively, these processes and associated forms and deposits have contributed variously to preservation, development, or degradation of permafrost and ground ice. The multifaceted Late Quaternary history in this region has impeded understanding of the distributions of ice-cored topography and RTS. For example, rather than glaciogenic ice, the long reigning regional model for ice-cored topography is according to post-glacial development of intrasedimental segregation-intrusion ice. Toward better understanding the evolution of the whole landscape and the distribution of climate-sensitive terrain, we use a landsystems approach as a means to understand how the ice-cored topography developed where RTS form, through analysing the cryostratigraphy. To this end, we identify 6 RTS representing a suite of ice-cored topographic settings, including: (i) preserved basal glacial ice facies within clayey diamict that has been thrusted and folded by glacial push representing morainal deposits of the Sitidgi Stade; (ii) ice contact outwash sediments associated with the Sitidgi Stade, overlying a thermo-erosional contact with underlying basal glacial icy diamict of the Toker Point Stade; (iii) deformed basal glacial ice, eroded down by meltwater-deposited outwash sands some time between the Toker Point and Sitidgi Stades (could be ca. 12.9 kyr BP); (iv) massive, undeformed segregation-intrusion basal ice, likely formed subglacially by freezing of intrasedimental water in pre-existing Pleistocene sands into the base of the glacier, overlain by glacial diamicton; (v) deformed basal ice facies of intermediate Toker Point – Sitidgi Stades, with an upper layer that may be supra-glacial melt-out till into which segregated ice formed; and (vi) segregation ice that formed as permafrost aggraded into glaciolacustrine clays deposited in proglacial or glacially dammed basins, that was subsequently eroded down by glaciofluvial outwash (Sitidgi Stade). To summarize, the distribution of RTS reflects primarily the distribution of icy basal glacial diamict preserved in moraines, but also basal ice and icy basal diamict that are preserved beneath glaciofluvial deposits, segregation ice in glaciolacustrine deposits, and massive segregation-intrusion ice in Pleistocene sands beneath a till plain.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.054
Threshold uncertainty score0.149

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0020.002
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.055
GPT teacher head0.218
Teacher spread0.163 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2020
Admission routes2
Has abstractyes

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