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Record W4220789509 · doi:10.1073/pnas.2119105119

Rapid seafloor changes associated with the degradation of Arctic submarine permafrost

2022· article· en· W4220789509 on OpenAlexafffundabout
C. K. Paull, S R Dallimore, Young Keun Jin, David W. Caress, E. M. Lundsten, R. Gwiazda, K. Anderson, John E. Clark, Scott Youngblut, Humfrey Melling

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsCanadian Hydrographic ServiceFisheries and Oceans CanadaGeological Survey of CanadaNatural Resources Canada
FundersNational Research Council CanadaGovernment of CanadaDavid and Lucile Packard Foundation
KeywordsPermafrostSeafloor spreadingGeologyArcticBathymetrySubmarineContinental shelfOceanographyBathymetric chartPleistocenePhysical geographyGeomorphologyPaleontologyGeography

Abstract

fetched live from OpenAlex

SignificanceTemperature increases in Arctic regions have focused attention on permafrost degradation on land, whereas little is known about the dynamics of extensive glacial-age permafrost bodies now submerged under the vast Arctic Continental shelves. Repeated high-resolution bathymetric surveys show that extraordinarily rapid morphologic changes are occurring at the edge of the continental slope of the Canadian Beaufort Sea along what was once the seaward limit of relict Pleistocene permafrost. How widespread similar changes are on the Arctic shelves is unknown, as this is one of the first areas in the Arctic subjected to multiple multibeam bathymetric surveys. Rapid morphologic changes associated with active submarine permafrost thawing may be an important process in sculpturing the seafloor in other submarine permafrost settings.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.061
GPT teacher head0.256
Teacher spread0.196 · 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

Citations35
Published2022
Admission routes3
Has abstractyes

Explore more

Same venueProceedings of the National Academy of SciencesSame topicClimate change and permafrostFrench-language works237,207