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Record W7111663223

Decadal Glacier Geometry and Mass Changes in Auyuittuq and Sirmilik National Parks, Canadian Arctic (1958–2024)

2025· dissertation· en· W7111663223 on OpenAlexaboutno aff

Bibliographic record

VenueQSpace (Queen's University Library) · 2025
Typedissertation
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsnot available
Fundersnot available
KeywordsGlacierGlacier mass balanceDigital elevation modelElevation (ballistics)ArcticAltitude (triangle)Tidewater glacier cycleSnowGeodetic datum
DOInot available

Abstract

fetched live from OpenAlex

Long-term baseline data and interannual monitoring of glacier change in the Canadian Arctic Archipelago (CAA) remain sparse. This dissertation quantifies multi-decadal to recent interannual glacier changes in Auyuittuq National Park (ANP) and Sirmilik National Park (SNP), and presents a scalable framework for operational monitoring in data-sparse polar environments. Historical glacier geometry and elevation change were reconstructed from 1958/59 aerial photographs using photogrammetric processing, co-registered to contemporary digital elevation models (DEMs; ArcticDEM v4.1 and TanDEM-X) to derive geodetic mass balance for nine glaciers (1958–2022), with bias correction and uncertainty quantification. For interannual variability (2013–2024), a hybrid machine-learning approach—K-means pseudo-labelling followed by Random Forest classification—was applied to multispectral imagery to delineate snowline altitude (SLA) and snow cover ratio (SCR). An automated pipeline extracted continuous records of SLA, snow-covered area (SCA), and accumulation-area ratio (AAR) from 9,919 Sentinel-1/2 and Landsat 8/9 scenes, incorporating masking of clouds, shadows, and off-glacier areas. Validation was performed using in situ equilibrium-line altitude (ELA) from White Glacier. Widespread surface lowering and area loss occurred since 1958, most pronounced at low-elevation glacier tongues. Specific geodetic mass balances for six ANP glaciers ranged from −0.22 to −0.35 m w.e. a⁻¹ (1959–2021/22), while Fountain Glacier (SNP) averaged −0.35 ± 0.02 m w.e. a⁻¹ (1958–2022). Glacier length shortened by ~7–16 % and area decreased by ~9–28 % in ANP; in SNP, length loss was smaller (~2 %) with ~5–14 % area reduction. Co-registration yielded near-zero median elevation differences and NMADs of 4.07 m (ANP) and 2.08 m (SNP). The classifier achieved >99.9 % accuracy; remotely sensed SLA strongly correlated with ELA from 2019–2024 (r = 0.994; RMSE = 30 m). Late-summer SLA rose consistently from 2013 to 2024, co-varying with positive degree days (PDD) and modulated by glacier hypsometry. SLA sensitivity to PDD ranged from 0.67 to 2.56 m (°C d)⁻¹. SLA is shown to be a robust, transferable proxy for annual mass balance where late-season imagery is available. This study establishes a defensible long-term glacier monitoring baseline and an operational pathway for data-limited polar regions, highlighting vulnerability of low-elevation glaciers on Baffin and Bylot Islands.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.019
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.008
GPT teacher head0.182
Teacher spread0.173 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes1
Has abstractyes

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