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Record W2548955766 · doi:10.1017/jog.2016.112

Comparison of geodetic and glaciological mass budgets for White Glacier, Axel Heiberg Island, Canada

2016· article· en· W2548955766 on OpenAlexafffundabout
Laura Thomson, Michael Zemp, Luke Copland, J. Graham Cogley, M. A. Ecclestone

Bibliographic record

VenueJournal of Glaciology · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsTrent UniversityUniversity of Ottawa
FundersW. Garfield Weston FoundationNatural Sciences and Engineering Research Council of CanadaMcGill UniversityArcticNetUniversity of Ottawa
KeywordsGlacierGlacier mass balanceGeologyGeodetic datumArcticPhysical geographyPeriod (music)Balance (ability)GeodesyGeomorphologyOceanographyGeography

Abstract

fetched live from OpenAlex

ABSTRACT This study presents the first reanalysis of a long-term glacier mass-balance record in the Canadian Arctic. The reanalysis is accomplished through comparison of the 1960–2014 glaciological mass-balance record of White Glacier, Axel Heiberg Island, Nunavut, with a geodetically derived mass change over the same period. The corrections applied to homogenize the two datasets, including adjusting for changes in hypsometry over the period of record and the generic differences between methods, are discussed along with the associated systematic and random errors of the two forms of mass-balance measurement. Statistical comparison of the two datasets reveals that within the error margin there is no significant difference between the average annual glaciological balance (–213 ± 28 mm w.e. a −1 ) and geodetic balance (–178 ± 16 mm w.e. a −1 ) at White Glacier over the 54 year record. The validity of this result, and the assumptions made in implementing the glaciological method, are critically assessed.

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 score0.210
Threshold uncertainty score0.884

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 head0.026
GPT teacher head0.260
Teacher spread0.234 · 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

Citations42
Published2016
Admission routes3
Has abstractyes

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