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Record W4414739127 · doi:10.5463/thesis.1377

Eroding Arctic coasts

2025· dissertation· en· W4414739127 on OpenAlexaboutno aff
Fleur C J van Crimpen

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
FundersNederlandse Organisatie voor Wetenschappelijk Onderzoek
KeywordsPermafrostArcticTotal organic carbonCarbon fibersGreenhouse gasThe arcticCoastal erosion

Abstract

fetched live from OpenAlex

The Arctic is experiencing rapid warming, nearly four times faster than the global average. This region is known for its permafrost, ground that has remained frozen or stayed at 0°C for at least two consecutive years and stores vast amounts of organic carbon. In fact, permafrost contains nearly twice as much carbon as is currently present in the atmosphere. However, as temperatures rise and the ground begins to thaw, this once-trapped carbon is released into the environment. One of the most visible and impactful consequences of rapid thaw is coastal erosion, which transports organic material from land into the Arctic Ocean. In this study, we investigate how coastal erosion contributes to the release of organic carbon and what happens to it once it enters the marine system. Understanding the fate of this carbon, whether it becomes buried in sediments or breaks down during transport and becomes a greenhouse gas like CO₂, is critical for improving our understanding of permafrost carbon feedback loops. To study these processes, we used a method called hydrodynamic fractionation, which allows us to separate and track different carbon fractions based on their size, density, and interaction with minerals. Using geochemical analysis, we further determined the age (Δ¹⁴C), composition (δ¹³C), and reactivity of the different fractions of organic carbon. This approach helps us understand how carbon moves through the system, where it is resuspended, degraded, or deposited, and which fractions are most likely to contribute to CO₂ emissions. One of our key findings is that the nearshore zone (waters less than 5 meters deep), located just off the eroding coastline, is critically undersampled. Only 6% of sediment samples in the Arctic basin are collected in this shallow zone. Despite covering just a small portion of the Arctic Ocean, the nearshore zone plays a key role in carbon cycling. Waves and currents frequently resuspend freshly eroded material, exposing it to oxygen and microbes that accelerate degradation. Much of this material consists of unprotected vascular plant debris, which holds organic carbon that is particularly prone to breaking down. This means the nearshore is not just a transition zone, it is a hotspot for carbon degradation. However, logistical challenges make it difficult to sample these shallow, remote waters, resulting in a major research gap. Beyond carbon, this research has significant implications for the people who live in the Arctic. Indigenous communities, such as those in Tuktoyaktuk in Canada’s Northwest Territories, depend on the stability of permafrost for homes, food storage, and cultural practices. As the ground thaws, coastlines collapse, ecosystems shift, and traditional travel and hunting routes become increasingly dangerous. As the Arctic climate continues to change, both carbon stocks and communities are at risk. This study underscores the need to more closely monitor vulnerable coastal zones, improve our climate feedback models, and integrate local and Indigenous knowledge to build a more complete understanding of a rapidly changing Arctic.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.285
Threshold uncertainty score0.567

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.001
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.007

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 head0.048
GPT teacher head0.433
Teacher spread0.385 · 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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