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Record W7052204485

Remote Sensing Analysis of Recent Coastal Change and Controlling Factors in Tuktoyaktuk Peninsula (Beaufort Sea Coast, Canada)

2022· dissertation· en· W7052204485 on OpenAlexaboutno aff

Bibliographic record

VenueRepositório da Universidade de Lisboa (University of Lisbon) · 2022
Typedissertation
Languageen
FieldEngineering
TopicPlasma Diagnostics and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsTundraClimate changeIntertidal zonePermafrostSubaerialShoreCoastal managementCoastal hazardsArcticSea level
DOInot available

Abstract

fetched live from OpenAlex

The average rate of coastal change in the Arctic Ocean is -0.5 m/yr, despite significant local and regional variations, with large areas well above -3 m/yr. Recent data suggest an acceleration of coastal retreat in specific areas due to an increasingly shorter sea ice season, higher storminess, warmer ocean waters and sea-level rise. Moreover, climate warming is inducing the subaerial degradation of permafrost and increasing land to sea sediment transportation. This work consists of the characterization and analysis of the main controlling factors influencing recent coastline change in the Tuktoyaktuk Peninsula, Northwest Territories, Canada. The specific objectives are: I. mapping Tuktoyaktuk Peninsula’s coastline at different time-steps using remote sensing imagery, II. quantifying the recent coastal change rates, III., characterizing the coastal morphology, IV. identifying the main controlling factors of the coastal change rates. A very high-resolution Pleiades survey from 2020, aerial photos from 1985 and the ArcticDEM were used. Results have shown an average coastline change rate of -1.06 m/yr between 1985 and 2020. While this number is higher than the Arctic average rate, it neglects to show the significance of extreme cases occurring in specific areas. Tundra cliffs are the main coastal setting, occupying c. 56% of the Tuktoyaktuk Peninsula coast and foreshore beaches represent 51%. The results display an influence of coastal geomorphology on change rates. The coastal retreat had higher values in backshore tundra flats (-1.74 m/yr), whereas aggradation concentrates in barrier beaches and sandspits (-0.81 m/yr). The presence of ice-wedge polygons contributes to increasing cliff retreat. Foreshore assessment may be crucial, as beaches present a hindering impact on coastal retreat (-0.76 m/yr), whereas foreshore tundra flats promote it (-1.74 m/yr). There are 48 areas with retreat rates higher than -4 m/yr, most being submersion cases. This dissertation identifies the interconnection between coastal geomorphology and change rates, raising attention to the distinct impacts of erosion and submersion processes.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.377
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
Bibliometrics0.0010.001
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 head0.202
Teacher spread0.187 · 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 designSimulation or modeling
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
Published2022
Admission routes1
Has abstractyes

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