MétaCan
Menu
Back to cohort
Record W3040734639 · doi:10.1139/as-2020-0002

Morphological and evolutionary patterns of emerging arctic coastal landscapes: the case of northwestern Nunavik (Quebec, Canada)

2020· article· en· W3040734639 on OpenAlexaffvenueabout
Antoine Boisson, Michel Allard

Bibliographic record

VenueArctic Science · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversité LavalCenter for Northern Studies
Fundersnot available
KeywordsLandformTundraPermafrostMoraineGeologyPhysical geographyCoastal geographyShoreCoastal erosionGlacierAggradationGlacial periodArcticClimate changeWetlandErosionGeomorphologyOceanographyGeographyFluvialEcology

Abstract

fetched live from OpenAlex

Northwestern Nunavik (Quebec, Canada) is characterized by specific landforms and poorly documented examples of emerging coastal landscapes. In this study, we identified the different types of coasts and examined how they were morphologically reworked and shaped during the Holocene. This coastal region is currently emerging at rates of 8–9 mm/year due to glacial isostatic adjustment. The coastal zone includes a large number of glacial and glaciofluvial landforms such as De Geer moraines, eskers, and drumlinoid ridges that are continuously modified by coastal processes as they emerge. Wave erosion, shore drifting, and sedimentation transform the original landforms into transverse spits, tombolos, dunes, beaches, and narrow tidal flats. Once raised above the reach of storm surges, the coastal landscape evolves into a maze of low tundra ridges, wetlands, and lakes, which represent the end point of rapid shoreline regression. Exposure to a cold climate allows permafrost inception and aggradation in the uplifted sediments, forming features such as ice-wedge polygons and frost boils. Conceptual models of coastal evolution and ecosystem formation are proposed, from the original submarine landscapes to the emerged landscapes.

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 score0.038
Threshold uncertainty score0.997

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 studies0.0000.000
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 head0.027
GPT teacher head0.228
Teacher spread0.201 · 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

Citations5
Published2020
Admission routes3
Has abstractyes

Explore more

Same venueArctic ScienceSame topicClimate change and permafrostFrench-language works237,207