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Record W3024695573 · doi:10.1029/2020gl087695

A Conceptual Model for Anticipating the Impact of Landscape Evolution on Groundwater Recharge in Degrading Permafrost Environments

2020· article· en· W3024695573 on OpenAlexafffundabout
Nathan Young, Jean‐Michel Lemieux, Hugo Delottier, Richard Fortier, Philippe Fortier

Bibliographic record

VenueGeophysical Research Letters · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversité LavalCenter for Northern Studies
FundersNatural Sciences and Engineering Research Council of CanadaCanada First Research Excellence FundUniversité Laval
KeywordsPermafrostGroundwater rechargeThermokarstGroundwaterWater tableVegetation (pathology)Hydrology (agriculture)TundraEnvironmental scienceGeologyArcticSubarctic climatePhysical geographyAquiferGeographyGeotechnical engineeringOceanography

Abstract

fetched live from OpenAlex

Abstract Temperatures in the arctic and subarctic are rising at more than twice the rate of the global average, driving the accelerated thawing of permafrost across the region. The impacts of permafrost degradation have been studied in the discontinuous permafrost zone at Umiujaq, in northern Quebec, Canada, for over 30 years, but the effects of changing land cover on groundwater recharge are not well understood. The water table fluctuation method was used to compute groundwater recharge using 4 years of water level data and soil moisture readings from five field sites characteristic of different stages of permafrost degradation and vegetation invasion. Results indicate that as vegetation grows taller, groundwater recharge increases, likely due to increased snow thickness. Results were then combined with a preexisting conceptual model that describes the evolution from tundra to shrubland and forests to create a new model for describing how groundwater recharge is affected by landscape evolution.

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)0.001
metaresearch head score (Gemma)0.002
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: none
Teacher disagreement score0.052
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.163
GPT teacher head0.345
Teacher spread0.182 · 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

Citations30
Published2020
Admission routes3
Has abstractyes

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Same venueGeophysical Research LettersSame topicClimate change and permafrostFrench-language works237,207