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Record W2761632597 · doi:10.1139/as-2016-0011

Ice wedge degradation and CO2 and CH4 emissions in the Tuktoyaktuk Coastlands, NT

2017· article· en· W2761632597 on OpenAlexafffundvenueabout
Abra F. Martin, Trevor C. Lantz, Elyn Humphreys

Bibliographic record

VenueArctic Science · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsCarleton UniversityUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPermafrostIce wedgeAtmospheric sciencesEnvironmental scienceMethaneTerrainCarbon dioxideAtmosphere (unit)Greenhouse gasPeatCarbon fibersMoistureGeologySoil scienceMeteorologyMaterials scienceChemistry

Abstract

fetched live from OpenAlex

Increases in ground temperature make soil organic carbon in permafrost environments highly vulnerable to release to the atmosphere. High-centred polygonal terrain is a form of patterned ground that may act as a large source of carbon to the atmosphere because thawing ice wedges can result in increased ground temperatures, soil moisture, and thaw depth. To evaluate the effect of ice wedge degradation on carbon flux, carbon emissions were characterized at two polygonal peatlands in the Tuktoyaktuk Coastlands in northern Canada. Opaque chambers were used to measure CO2 and CH4 emissions from nine nondegraded polygon centres and nine moderately degraded troughs four times during the growing season. To measure emissions from 10 ponds resulting from severe ice wedge degradation, wind diffusion models were used to characterize fluxes using CO2 and CH4 concentration measurements made in each pond. Our field data show that degraded troughs had increased ground temperature, deeper active layers, and increased CO2 and CH4 emissions. Our study shows that rates of CO2 and CH4 emissions from high-centreed polygonal terrain are likely to increase with more widespread melt pond formation in this terrain type.

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)0.001
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 score0.013
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.060
GPT teacher head0.285
Teacher spread0.225 · 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

Citations13
Published2017
Admission routes4
Has abstractyes

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