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Record W2965038815 · doi:10.1088/1748-9326/ab3947

Soil degradation determines release of nitrous oxide and dissolved organic carbon from peatlands

2019· article· en· W2965038815 on OpenAlexaffabout
Haojie Liu, Fereidoun Rezanezhad, Bernd Lennartz

Bibliographic record

VenueEnvironmental Research Letters · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsUniversity of Waterloo
FundersEuropean Social FundDeutsche Forschungsgemeinschaft
KeywordsPeatNitrous oxideDegradation (telecommunications)Dissolved organic carbonEnvironmental scienceSoil carbonEnvironmental chemistryCarbon fibersSoil retrogression and degradationSoil scienceChemistrySoil waterMaterials scienceEcology

Abstract

fetched live from OpenAlex

Abstract Carbon (C) and nitrogen (N) release from peatlands are closely related to water management and soil degradation. However, peat degradation has not been explicitly accounted for when estimating national greenhouse gas inventories. Here, we assembled a comprehensive dataset covering European, Russian and Canadian peatlands and introduced soil bulk density (BD) as a proxy for peat degradation to estimate nitrous oxide (N 2 O) and dissolved organic carbon (DOC) release. The results show that physical and biogeochemical properties of peat are sensitive to soil degradation. The BD is superior to other parameters (C/N, pH) to estimate annual N 2 O emissions and DOC pore water concentrations. The more a peat soil is degraded, the higher the risk of air/water pollution in peaty landscapes. Even after rewetting, highly degraded soils may exhibit high N 2 O release rates. The estimated annual N 2 O–N emissions from European, Russian and Canadian degraded peatlands sum up to approximately 81.0 Gg. The derived BD-based functions can assist in computing global matter fluxes from peatlands.

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 score0.442
Threshold uncertainty score1.000

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)0.0010.000

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 head0.011
GPT teacher head0.233
Teacher spread0.222 · 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

Citations67
Published2019
Admission routes2
Has abstractyes

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