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Record W2346302467 · doi:10.1002/esp.3960

Chemical depletion of sediment under the Greenland Ice Sheet

2016· article· en· W2346302467 on OpenAlexfundno aff
Joseph A. Graly, N. F. Humphrey, J. T. Harper

Bibliographic record

VenueEarth Surface Processes and Landforms · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsnot available
FundersNuclear Waste Management Organization
KeywordsBedrockGeologySedimentWeatheringGrain sizeChemical compositionClastic rockDissolutionSortingGeochemistryMineralogyIce sheetGeomorphologySedimentary rockChemistry

Abstract

fetched live from OpenAlex

Abstract The chemical composition of sediment sampled from a network of ice boreholes contacting the bed of the ablation zone of the Greenland Ice Sheet is compared to the composition of intact bedrock clasts. This sediment is enriched in silica and depleted in reactive cations compared to the underlying bedrock. In order to test whether these differences result from grain size biases either in sample collection or due to natural sorting, sediment samples were analyzed by grain size aliquots. Enrichment of silicon and depletion of cations is consistent across grain size classes and the compositions of bedrock and subglacial sediment are statistically separate. The difference in composition between subglacial sediment and rock aligns closely with the composition of dissolved solutes in waters sampled from the same field sites and is dissimilar to the composition of the sites' suspended sediment. This implies that chemical weathering rather than disproportionate physical removal of friable minerals is responsible for the compositional differences between rock and sediment. Mass balance analysis implies 3–10% of the sediment's mass is lost to solute dissolution, with approximately double that amount precipitated as clay minerals (a large portion of which may have been physically expelled). This result implies that temperate ice sheet subglacial environments may be more chemically active than previously realized. Copyright © 2016 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 score0.007
Threshold uncertainty score0.892

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)0.0010.000

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 head0.014
GPT teacher head0.226
Teacher spread0.212 · 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

Citations16
Published2016
Admission routes1
Has abstractyes

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