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Record W3045508302 · doi:10.1139/as-2019-0026

Geomorphological controls over carbon distribution in permafrost soils: the case of the Narsajuaq river valley, Nunavik (Canada)

2020· article· en· W3045508302 on OpenAlexaffvenueabout
Samuel Gagnon, 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
KeywordsPermafrostLandformSoil carbonSoil waterPhysical geographyEnvironmental scienceCarbon fibersCarbon cycleHydrology (agriculture)Total organic carbonSoil scienceGeologyGeomorphologyEnvironmental chemistryGeographyEcologyOceanographyEcosystem

Abstract

fetched live from OpenAlex

Soils in the northern circumpolar region play a central role in the global carbon cycle because the release of carbon through permafrost thaw and geomorphological disturbances can potentially cause a feedback on climate. However, large uncertainties in estimates of permafrost carbon stocks remain, mainly because of wide gaps in the spatial coverage of soil carbon sampling sites and the large mapping polygons used to upscale data. By combining mapping of landforms and knowledge of surficial geology to upscale soil carbon content measurements, we provide an assessment of soil total carbon content in the region of the Narsajuaq river valley (Nunavik, Canada) to generate the first high-resolution soil carbon estimate confirmed by field measurements in Nunavik. We estimate that the Narsajuaq river valley and the surrounding uplands have a weighted average of 3.4 kg C m −2 (0–100 cm), with 73% of the total carbon content stored in the top 30 cm. The results also indicate that the valley is a carbon hotspot in the region, containing 76% of the total carbon content (0–100 cm) of the study area. Although soil carbon estimates will always require field sampling, the geomorphological mapping approach can significantly improve carbon content estimates and provide better inputs for models.

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 score0.052
Threshold uncertainty score0.975

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.028
GPT teacher head0.230
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

Citations6
Published2020
Admission routes3
Has abstractyes

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