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Record W2937388604 · doi:10.1029/2019gl082611

Pathways for Ecological Change in Canadian High Arctic Wetlands Under Rapid Twentieth Century Warming

2019· article· en· W2937388604 on OpenAlexafffundabout
Thomas G. Sim, Graeme T. Swindles, Paul J. Morris, Mariusz Gałka, Donal Mullan, Jennifer M. Galloway

Bibliographic record

VenueGeophysical Research Letters · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsGeological Survey of CanadaNatural Resources CanadaCarleton University
FundersNatural Resources CanadaPolar Knowledge CanadaNatural Environment Research CouncilUniversity of Ottawa
KeywordsArcticWetlandClimate changeEnvironmental scienceGlobal warmingEnvironmental changeEcologyArctic ecologyThe arcticPermafrostOceanographyPhysical geographyClimatologyGeographyEarth scienceGeologyBiology

Abstract

fetched live from OpenAlex

Abstract We use paleoecological techniques to investigate how Canadian High Arctic wetlands responded to a mid‐twentieth century increase in growing degree days. We observe an increase in wetness, moss diversity, and carbon accumulation in a polygon mire trough, likely related to ice wedge thaw. Contrastingly, the raised center of the polygon mire showed no clear response. Wet and dry indicator testate amoebae increased concomitantly in a valley fen, possibly relating to greater inundation from snowmelt followed by increasing evapotranspiration. This occurred alongside the appearance of generalist hummock mosses. A coastal fen underwent a shift from sedge to shrub dominance. The valley and coastal fens transitioned from minerogenic to organic‐rich wetlands prior to the growing degree days increase. A subsequent shift to moss dominance in the coastal fen may relate to intensive grazing from Arctic geese. Our findings highlight the complex response of Arctic wetlands to warming and have implications for understanding their future carbon sink potential.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.151
Threshold uncertainty score0.304

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
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 head0.091
GPT teacher head0.298
Teacher spread0.208 · 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

Citations41
Published2019
Admission routes3
Has abstractyes

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