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Record W2885863043 · doi:10.1002/esp.4302

Composition and origin of a lithalsa related to lake‐level recession and Holocene terrestrial emergence, Northwest Territories, Canada

2017· article· en· W2885863043 on OpenAlexaffabout
Adrian J. Gaanderse, S A Wolfe, C. R. Burn

Bibliographic record

VenueEarth Surface Processes and Landforms · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsGeological Survey of CanadaCarleton University
Fundersnot available
KeywordsHoloceneGeologyPrecipitationAlluviumPhysical geographyGeomorphologyGeochemistryOceanographyGeography

Abstract

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Abstract Lithalsas of the Great Slave Lowland, Northwest Territories, occur within fine‐grained glaciolacustrine, lacustrine, and alluvial deposits. Detailed investigations of a lithalsa revealed that it is composed of ice‐rich sediments with ice lenses up to 0.2 m thick below 4 m depth. The observed ice accounted for about 2 m of the 4 m between the top of the lithalsa and adjacent terrain. The ice is isotopically similar to modern surface water, but enriched in δ 18 O relative to local precipitation. Total soluble cation concentrations are low in the basal, Shield‐derived and unweathered glaciolacustrine sediments of the lithalsa. Higher concentrations in the overlying Holocene‐aged lacustrine and alluvial deposits may be due to greater ion availability in Holocene surface waters. Increasing Cl ‐ and Na + concentrations in clays at depth likely relate to exclusion and migration of these dissolved ions in pore water during ice lens formation though total soluble cations remain comparatively low. The lithalsa developed 700 to 300 cal yr BP. A conceptual model of lithalsa formation and landscape evolution illustrates that this feature and more than 1800 other lithalsas in the region have developed in association with Holocene terrestrial emergence following lake‐level recession. Copyright © 2017 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 score0.300
Threshold uncertainty score0.384

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 head0.031
GPT teacher head0.255
Teacher spread0.224 · 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

Citations11
Published2017
Admission routes2
Has abstractyes

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