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Record W2010460814 · doi:10.1016/j.yqres.2009.07.006

Variations in Sediment yield Over the Advance and Retreat of a Calving Glacier, Laguna San Rafael, North Patagonian Icefield

2009· article· en· W2010460814 on OpenAlexaff
Michèle Koppes, R.E. Sylwester, Andrés Rivera, B. Hallet

Bibliographic record

VenueQuaternary Research · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsGeologyGlacierErosionSedimentTidewaterGlacial periodGeomorphologyLandformDeposition (geology)Tidewater glacier cyclePhysical geographyBathymetryIce fieldOceanographyIce calving

Abstract

fetched live from OpenAlex

Bathymetric and sub-bottom acoustic data were collected in Laguna San Rafael, Chile, to determine sediment yields during the Little Ice Age advance and subsequent retreat of San Rafael Glacier. The sediment volumes and subaqueous landforms imaged are used to interpret the proglacial dynamics and estimate erosion rates from a temperate tidewater glacier over a complete advance–retreat cycle. Sediment yields from San Rafael Glacier averaged 2.7 × 10 m/a since the end of the Little Ice Age, circa AD 1898, corresponding to average basin-wide erosion rates of 23 ± 9 mm/a; the highest erosion rates, 68 ± 23 mm/a, occurred at the start of the retreat phase, and have since been steadily decreasing. Erosion rates were much lower during glacial advance, averaging at most 7 mm/a, than during retreat. Such large glacial sediment yields over two centuries of advance and retreat suggest that the contribution of sediments stored subglacially cannot account for much of the sediment being delivered to the terminus today. The detailed sub-bottom information of a proglacial lagoon yields important clues as to the timing of erosion, deposition and transfer of glacigenic sediments from orogens to the continental shelves, and the influence of glacier dynamics on this process.

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)0.001
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 score0.014
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.045
GPT teacher head0.318
Teacher spread0.272 · 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

Citations40
Published2009
Admission routes1
Has abstractyes

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