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Record W1982083897 · doi:10.3189/172756405781813753

The chemical composition of runoff from Canada Glacier, Antarctica: implications for glacier hydrology duringa cool summer

2005· article· en· W1982083897 on OpenAlexaboutno aff
Martyn Tranter, Andrew G. Fountain, W. Berry Lyons, Thomas Nylen

Bibliographic record

VenueAnnals of Glaciology · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicPolar Research and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsGeologyGlacierSurface runoffChemical compositionHydrology (agriculture)SedimentOceanographyGeomorphologyChemistry

Abstract

fetched live from OpenAlex

Abstract Variations in the chemical composition of subsurface runoff within Canada Glacier, Antarctica, are used to identify the main source waters, which are vertical surfaces, and melt from solar-heated debris within channels, cryoconite holes and pools. The main flow paths are subsurface connections between cryoconite holes, pools and riffles. The latter may become partially disconnected during hard freeze. The chemical composition of runoff at the outlet of Canada Glacier during January 2000 was dominated by Ca 2+ , HCO 3 – and sea salt (Na + and Cl – ), and became depleted in sea-salt and non-sea-salt (*) SO 4 2– as the subsurface drainage system in a frozen pool-and-riffle system was flushed and the melting ice surface became depleted of overwinter dry deposited salts. Only during 2 days of hard freeze did sea salt and *SO 4 2– increase in concentration together. Otherwise, sea salt and *SO 4 2– declined while *Ca 2+ and HCO 3 – increased. The latter ions are derived from the chemical weathering of sediment in frozen-topped pools, channels and cryoconite holes. It is inferred that the hydrochemical processes which occur in the vestigial, subsurface drainage system are the elution of ions from ice melt, dilution of these ions downstream by ice melt from vertical surfaces and the dissolution of dust, in subsurface pools, channels and/or cryoconite holes.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.534
Threshold uncertainty score0.927

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 studies0.0000.001
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 head0.035
GPT teacher head0.308
Teacher spread0.273 · 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

Citations13
Published2005
Admission routes1
Has abstractyes

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