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Record W1987215000 · doi:10.3189/172756405781813528

Changes in geometry and subglacial drainage derived from digital elevation models: Unteraargletscher, Switzerland, 1927–97

2005· article· en· W1987215000 on OpenAlexaff
Urs H. Fischer, André Braun, Andreas Bauder, Gwenn E. Flowers

Bibliographic record

VenueAnnals of Glaciology · 2005
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsGeologyMoraineGlacierGeomorphologyChannelizedDigital elevation modelThinningDrainageDebrisElevation (ballistics)GeometryOceanographyRemote sensing

Abstract

fetched live from OpenAlex

Abstract Digital elevation models of the bed and surface of Unteraargletscher, Switzerland, are used to reconstruct the theoretical pattern of basal water drainage for the years 1927, 1947, 1961 and 1997, during which period the glacier was thinning and receding. The theoretical drainage pattern for 1997 compares well, in a broad sense, with the locations of active moulins and the hydraulic connection status of boreholes drilled to the glacier bed. Changes in the basal water-flow pattern over the period 1927–97 that are revealed by the theoretical reconstructions of the subglacial drainage system structure are likely to have resulted from changes in glacier geometry. Concurrent with the retreat and thinning of the glacier, the height of medial moraines increased, probably due to the insulating effect of the debris cover reducing the melt of the underlying ice. This increase of moraine heights has led to the formation of hydraulic barriers at the glacier bed such that water flow has become channelized beneath the ice along drainage axes that parallel the course of the medial moraines on the glacier surface.

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 score0.111
Threshold uncertainty score0.220

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.059
GPT teacher head0.259
Teacher spread0.199 · 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".

Quick stats

Citations20
Published2005
Admission routes1
Has abstractyes

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