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Record W2898468399 · doi:10.1080/04353676.2018.1522958

Revisiting glaciological measurements on Haupapa/Tasman Glacier, New Zealand, in a contemporary context

2018· article· en· W2898468399 on OpenAlexfundno aff
Heather Purdie, Brian Anderson, Andrew Mackintosh, Wendy Lawson

Bibliographic record

VenueGeografiska Annaler Series A Physical Geography · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsnot available
FundersNational Oceanic and Atmospheric AdministrationVictoria UniversityFoundation for Research, Science and TechnologyDepartment of Conservation, New ZealandUniversity of VictoriaUniversity of CanterburyComer Science and Education Foundation
KeywordsGlacierContext (archaeology)GeologyGeodesyGeographyPhysical geographyArchaeology

Abstract

fetched live from OpenAlex

Compilation of fragmented glaciological data, spanning more than a century at Haupapa/Tasman Glacier, provides new insight on how this glacier is changing over time. Despite consistency in high accumulation on the glacier, dramatic surface thinning and up-glacier expansion of supraglacial debris highlights that the glacier is currently in disequilibrium with climate. However, pauses in the rate of debris emergence indicate that despite ongoing terminus retreat at the proglacial lake, a subtle response to climate is still detectable mid-glacier. Analysis of surface velocity data at key locations reveals no trend over time at the Malte Brun site in the upper ablation area, but recent deceleration was recorded near the Ball Glacier confluence, located 5 km up-glacier from the current terminus. Near-terminus acceleration during a period of rapid lake expansion, followed by more recent deceleration, demonstrates that at this time, ice thinning at Haupapa/Tasman Glacier is likely being driven by negative surface mass balance as opposed to dynamic thinning associated with proglacial lake enlargement.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.129
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.069
GPT teacher head0.263
Teacher spread0.194 · 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

Citations5
Published2018
Admission routes1
Has abstractyes

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