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Record W4362518665 · doi:10.24908/iqurcp16341

Changes in Glacial Area Extent in Auyuittuq National Park, Pangnirtung

2023· article· en· W4362518665 on OpenAlexaffvenueabout
Ashley Duyvesyeyn

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsQueen's University
Fundersnot available
KeywordsGlacierGlacial periodPhysical geographyArcticArchipelagoGeoreferenceNational parkAerial photographyPhotogrammetryAerial photosGeologyCartographyRemote sensingGeographyArchaeologyOceanographyGeomorphology

Abstract

fetched live from OpenAlex

With global temperatures rising four times faster in the Canadian Arctic compared to global averages, glaciers in the Canadian Arctic Archipelago (CAA) are starting to shrink at an alarming rate (Paul et al., 2020). To quantify rates of change, frequent updates of glacial outlines to provide an accurate database for monitoring are needed (Schaffer et al., 2017). Rundle, Nerutusoq, and Fort Beard Glaciers are all located on the Penny Ice Cap within Auyuittuk National Park, Baffin Island, Canada. Using a combination of aerial photography and ArcGIS Pro, I examined the historical and recent changes in glacial area extents. High resolution historical aerial photographs from the National Air Photo Library dating to September 1959 were manually georeferenced onto ArcGIS Pro. Photogrammetry techniques were then used to combine the aerial photographs into one combined orthographic image. The historic glaciers were then outlined using a manual technique based on the pixel size (8 μm) of the images using ArcGIS Pro. This allows me to compare glacial outlines from 2010 using the Randolph glacier inventory and 2022 using manual outline techniques described, respectively. Because using a manual technique can create accuracy limitation, specifically during the georeferencing process, I compared results for both time periods using an automatic method. Results from my work will provide estimates of changes in glacier area over time, the relative precision of different methods, and weather rates of shrinkage have increased over time. ReferencesPaul, F., Rastner, P., Azzoni, R. S., Diolaiuti, G., Fugazza, D., Le Bris, R., Nemec, J., Rabatel, A., Ramusovic, M., Schwaizer, G., & Smiraglia, C. (2020). Glacier shrinkage in the Alps continues unabated as revealed by a New Glacier Inventory from sentinel-2. Earth System Science Data, 12(3), 1805–1821. https://doi.org/10.5194/essd-12-1805-2020 Schaffer, N., Copland, L., & Zdanowicz , C. (2017). Ice velocity changes on Penny Ice Cap, Baffin Island, since the 1950s. Journal of Glaciology, 63(240), 716–730. https://doi.org/10.1017/jog.2017.40

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)0.000
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 score0.380
Threshold uncertainty score0.765

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.202
GPT teacher head0.361
Teacher spread0.159 · 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".

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Citations0
Published2023
Admission routes3
Has abstractyes

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