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Record W3009011915 · doi:10.1002/ppp.2033

Permafrost aggradation along the emerging eastern coast of Hudson Bay, Nunavik (northern Québec, Canada)

2020· article· en· W3009011915 on OpenAlexafffundabout
Antoine Boisson, Michel Allard, Denis Sarrazin

Bibliographic record

VenuePermafrost and Periglacial Processes · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversité LavalCenter for Northern Studies
FundersArcticNet
KeywordsPermafrostGeologyShoreBayOceanographyAggradationSubmarine pipelineSea levelPhysical geographyClimate changeGeomorphologyGeography

Abstract

fetched live from OpenAlex

Abstract Emerging polar coasts have different geothermal regimes than those in submergence. While the scientific community is mainly concerned with rapidly eroding permafrost coastlines in sedimentary formations where relative sea level is rising, much less research has been dedicated to permafrost dynamics in emergent coastal regions where post‐glacial uplift is ongoing. The eastern Hudson Bay coast of Nunavik (northern Québec, Canada) is undergoing glacio‐isostatic uplift at a current emergence rate of about 13 mm/yr, outpacing the current global sea‐level rise (~3 mm/yr) and progressively exposing new land to climate conditions favorable for permafrost formation. To observe incipient permafrost in the shore zone over time, in 2005 we strategically installed a thermistor cable down to a depth of 23 m at a high‐tide level site. We detected the formation and the continuing deepening of permafrost near the surface. Freezing of the ground was also favored by a succession of several cold years in Nunavik since 2010. The near 0°C temperature profile at greater depths also reveals the cooling influence of deep Hudson Bay waters on the shore zone ground temperature regime and the probable presence of subsea permafrost offshore of the measurement site.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.163
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.025
GPT teacher head0.222
Teacher spread0.198 · 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 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

Citations17
Published2020
Admission routes3
Has abstractyes

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