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Record W4318949260 · doi:10.1139/as-2022-0028

Shoreline change rates and land to sea sediment and soil organic carbon transfer in eastern Parry Peninsula from 1965 to 2020 (Amundsen Gulf, Canada)

2023· article· en· W4318949260 on OpenAlexafffundvenueabout
Rodrigue Tanguy, Dustin Whalen, Gonçalo Prates, Gonçalo Vieira

Bibliographic record

VenueArctic Science · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsNatural Resources Canada
FundersNatural Resources CanadaCrown-Indigenous Relations and Northern Affairs CanadaEuropean Commission
KeywordsPeninsulaOceanographyGeologyPARRYSedimentShoreTotal organic carbonPhysical geographyArchaeologyGeomorphologyGeographyEcology

Abstract

fetched live from OpenAlex

As the Arctic warms, permafrost coasts are eroding faster, threatening coastal communities, habitats, and altering sediment and nutrient budgets. The western Canadian Arctic is eroding at a rapid pace, however little is known on changes occurring in the Amundsen Gulf area. This study was conducted in the eastern coast of Parry Peninsula, a neglected rock-dominated coastal area. We used orthorectified aerial photos of 1965 and 1993 and very-high resolution satellite imagery of 2020 to manually delineate the shoreline according to backshore and foreshore centered approaches. Shoreline change rates were calculated and sediment and Organic Carbon transfer from land to sea estimated using digital elevation model, the Northern Circumpolar Soil Carbon Database and ground-ice content. The results show a mean erosion rate of 0.12 m/yr for the backshore zone and 0.16 m/yr for the foreshore zone, with increasing erosion in the Paulatuk Peninsula in recent decades. The average sediment transfer from land to sea was 20 m<sup>3</sup>/m/yr and the SOC flux was 7 kg C/m/yr. We highlight the importance of using the cliff-top as shoreline reference to accurately estimate sediment and SOC transfers, an approach neglected in automatic shoreline delineation techniques based on remote sensing imagery using the waterline.

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 score0.145
Threshold uncertainty score0.583

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.037
GPT teacher head0.242
Teacher spread0.204 · 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

Citations13
Published2023
Admission routes4
Has abstractyes

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