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EROSION MITIGATION DESIGN IN THE ARCTIC CONSIDERING CLIMATE CHANGE IMPACTS

2023· article· en· W4386960289 on OpenAlexaff
Fred Scott, F. Duckett, Lukas U. Arenson, Charles Klengenberg, Erwin Elias

Bibliographic record

VenueCoastal Engineering Proceedings · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsInuvialuit Regional CorporationBGC Engineering (Canada)University of Toronto
Fundersnot available
KeywordsRevetmentShoreCoastal erosionFlooding (psychology)PeninsulaErosionGeologyHarbourStorm surgeHAMLET (protein complex)StormOceanographyGeographyArchaeologyGeomorphology

Abstract

fetched live from OpenAlex

The Hamlet of Tuktoyaktuk is a low-lying peninsula in the Arctic on the Beaufort Sea that is vulnerable to coastal erosion and intermittent flooding. Most residences and buildings located near the coast have been relocated; those remaining are currently at risk of damage or destruction during storm events. In the longer term, cultural sites such as the graveyard are also at risk. Nearby Tuktoyaktuk Island, a beach/bluff system which shelters Tuktoyaktuk Harbour from waves, is eroding and if not protected may be gone by 2050. Baird was retained by the Hamlet of Tuktoyaktuk and Inuvialuit Regional Corporation (IRC) to assess erosion mitigation alternatives and select/implement a preferred design to protect the Hamlet and Island, which comprise a total shoreline length of approximately 2 km. Baird developed three design alternatives for shoreline protection, including articulated concrete block mattress (ACBM), concrete slab, and quarried armour stone revetments. The selected design is comprised of a quarried armour stone revetment along the entirety of the exposed shoreline of Tuktoyaktuk Island and the majority of the Tuktoyaktuk Hamlet shoreline.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.026
GPT teacher head0.223
Teacher spread0.197 · 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 designTheoretical or conceptual
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

Citations1
Published2023
Admission routes1
Has abstractyes

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