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Record W2330840791 · doi:10.3189/2014jog13j091

An evaluation of mass-balance methods applied to Castle creek Glacier, British Columbia, Canada

2014· article· en· W2330840791 on OpenAlexafffundabout
M. J. Beedle, Brian Menounos, Roger Wheate

Bibliographic record

VenueJournal of Glaciology · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Northern British Columbia
FundersCanadian Foundation for Climate and Atmospheric SciencesNatural Sciences and Engineering Research Council of CanadaGovernment of CanadaBritish Columbia Knowledge Development FundUniversity of Northern British Columbia
KeywordsGlacier mass balanceGlacierPhotogrammetryGeologySnowGeodesyBalance (ability)FirnPhysical geographyGeomorphologyClimatologyRemote sensingGeography

Abstract

fetched live from OpenAlex

Abstract We estimate the glacier mass balance of a 9.5 km 2 mountain glacier using three approaches for balance years 2009, 2010 and 2011. The photogrammetric, GPS and glaciological methods yielded sampling densities of 100, 5 and 2 points km -2 , with measurement precisions of ± 0.40, ± 0.10 and ± 0.10 m w.e. respectively. Our glaciological measurements likely include a positive bias, due to omission of internal and basal mass balance, and uncertainty in determining the interface between snow and firn with a probe (±0.10 m w.e.). Measurements from our photogrammetric method include a negative bias introduced by the manual operator and our temperature index model used to correct for different dates of imaging (0.15 m w.e.), whereas GPS measurements avoid these biases. The photogrammetric and GPS methods are suitable for estimating glacier-wide annual mass balance, and thus provide a valuable measure that complements the glaciological method. These approaches, however, cannot be used to estimate mass balance at a point or mass-balance profiles without a detailed understanding of the vertical component of ice velocity.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.245
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0010.000

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 head0.028
GPT teacher head0.291
Teacher spread0.263 · 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.

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

Citations48
Published2014
Admission routes3
Has abstractyes

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