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Record W4318316377 · doi:10.1017/jog.2022.119

Rapid demise and committed loss of Bowman Glacier, northern Ellesmere Island, Arctic Canada

2023· article· en· W4318316377 on OpenAlexafffundabout
Dorota Medrzycka, Luke Copland, Brice Noël

Bibliographic record

VenueJournal of Glaciology · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaNederlandse Organisatie voor Wetenschappelijk OnderzoekParks CanadaArcticNetUniversity of Ottawa
KeywordsGeologyGlacierGlacier mass balanceGlacier morphologyPhysical geographyArcticElevation (ballistics)Tidewater glacier cycleGeomorphologyOceanographyArctic ice packAntarctic sea iceGeographyIce calving

Abstract

fetched live from OpenAlex

Abstract Using historical and recent aerial photography and structure from motion (SfM) multiview stereo (MVS) techniques, we reconstruct the 1959 and 2018 ice surface topography and determine the geodetic mass balance of Bowman Glacier, a small mountain glacier on northern Ellesmere Island. This is combined with optical satellite imagery to reconstruct the evolution in extent of the glacier over six decades, and ground-penetrating radar measurements of ice thickness to estimate the remaining ice volume. Between 1959 and 2020, Bowman Glacier lost 78% of its extent (reducing from 2.75 to 0.61 km 2 ), while average annual area loss rates have nearly tripled in the past two decades. Over the 1959–2018 period, glacier-wide ice-thickness change averaged −22.7 ± 4.7 m, corresponding to a mean specific annual mass balance of −347.0 ± 71.4 mm w.e. a −1 . Projecting rates of area and volume change into the future indicates that the glacier will likely entirely disappear between 2030 and 2060. This study demonstrates the potential of SfM-MVS processing to generate elevation products from 1950/60s historical aerial photographs, and to extend observations of ice elevation and glacier volume change for the Canadian Arctic, prior to the satellite record.

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 score0.738
Threshold uncertainty score0.907

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 head0.015
GPT teacher head0.207
Teacher spread0.192 · 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

Citations11
Published2023
Admission routes3
Has abstractyes

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