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Record W2107703515 · doi:10.1002/hyp.7271

Seasonal hydrochemistry of a high Arctic wetland complex

2009· article· en· W2107703515 on OpenAlexafffundabout
Dan K. Thompson, Ming‐ko Woo

Bibliographic record

VenueHydrological Processes · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsMcMaster University
FundersNatural Resources CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsWetlandThermokarstPermafrostSnowmeltHydrology (agriculture)ArcticSurface runoffGeologyEnvironmental scienceLandformSurface waterPhysical geographySnowOceanographyEcologyGeomorphologyGeography

Abstract

fetched live from OpenAlex

Abstract The seasonal evolution in the hydrochemistry of four types of wetland (pond, ice wedge trough, wet meadow and mesic site) was studied in a 0·5 km 2 wetland complex in the Canadian High Arctic on the Fosheim Peninsula, Ellesmere Island. In the spring, a large influx of overland runoff from snowmelt quickly flushed away the solutes of the ice stored on the wetland surface over the winter, and homogenized the hydrochemistry across the entire wetland complex. As the surface flow receded, various wetland patches became hydrologically disconnected and their hydrochemical characteristics evolved differently. Although underlain by marine sediments and saline permafrost, solute concentrations in much of the wetland complex remained dilute compared with many Arctic wetlands. Through continued evaporation, melting of ground ice and localized thermokarst activities, the hydrochemistry of different wetland types acquired their distinctive characteristics as the summer progressed. This study demonstrates that large diversity in wetland hydrochemistry occurs even within a limited area, indicating the need to caution against generalizations based on limited spatial and temporal samples. Copyright © 2009 John Wiley & Sons, Ltd.

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 score0.015
Threshold uncertainty score0.985

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)0.0150.000

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 head0.045
GPT teacher head0.247
Teacher spread0.202 · 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

Citations21
Published2009
Admission routes3
Has abstractyes

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