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Record W2107749860 · doi:10.3189/172756406781812366

Accumulation at the equilibrium-line altitude of glaciers inferred from a degree-day model and tested against field observations

2006· article· en· W2107749860 on OpenAlexaff
Roger J. Braithwaite, S. C. B. Raper, Krys Chutko

Bibliographic record

VenueAnnals of Glaciology · 2006
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsQueen's University
FundersNatural Environment Research CouncilEuropean CommissionUniversity of Manchester
KeywordsGlacierDegree (music)Altitude (triangle)PrecipitationSnowmeltGlacier mass balanceClimatologyDegree dayGeologyAtmospheric sciencesRange (aeronautics)Lapse rateAir temperatureSnowEnvironmental scienceMeteorologyGeographyGeomorphologyMathematicsPhysics

Abstract

fetched live from OpenAlex

Abstract We extrapolate temperature data from a gridded climatology to the equilibrium-line altitude (ELA) of a glacier and tune a degree-day model by adjusting precipitation to give zero mass balance at the ELA. We verify the tuned model by comparing modelled accumulation with winter balance where this has been measured (presently for 180 glaciers). The modelled accumulation naturally depends upon the vertical lapse rate (VLR) for temperature and the degree-day factor (DDF) for snowmelt. Both are somewhat uncertain in high-mountain areas, but modelled accumulation and measured winter balance are in reasonable agreement for most glaciers. The degree-day model predicts a non-linear relation between accumulation and summer temperature at the ELA as assumed by many workers, but we find a family of curves rather than a single universal curve. Maritime glaciers with low annual temperature range have proportionally more accumulation than continental glaciers with high annual temperature range for a similar summer mean temperature. Averages of winter balance for the five main geographical regions where mass-balance data are available agree well with annual accumulation from the degree-day model.

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 score0.014
Threshold uncertainty score0.756

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 head0.179
GPT teacher head0.314
Teacher spread0.135 · 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

Citations33
Published2006
Admission routes1
Has abstractyes

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