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Record W2085963347 · doi:10.1080/00380768.2014.893812

Effects of nitrogen and sulfur deposition on CH<sub>4</sub>and N<sub>2</sub>O fluxes in high-altitude peatland soil under different water tables in the Tibetan Plateau

2014· article· en· W2085963347 on OpenAlexaff
Yongheng Gao, Huai Chen, Xiaoyang Zeng

Bibliographic record

VenueSoil Science & Plant Nutrition · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsUniversité du Québec à Montréal
FundersNational Key Research and Development Program of China
KeywordsPeatWater tableNitrogenEnvironmental chemistryDeposition (geology)SulfurPlateau (mathematics)SulfateAltitude (triangle)Nitrous oxideChemistryNitrateEnvironmental scienceAmmoniumTable (database)Surface waterHydrology (agriculture)GroundwaterEnvironmental engineeringGeologyEcology

Abstract

fetched live from OpenAlex

The effects of nitrogen (N) and sulfur (S) deposition on methane (CH4) and nitrous oxide (N2O) emissions under low (10 cm below soil surface) and high (at soil surface) water tables were investigated in the laboratory. Undisturbed soil columns from the alpine peatland of the Tibetan Plateau were analyzed. CH4 emission was higher and N2O emission was lower at the high water table than those at the low water table regardless of nutrient application. Addition of N (NH4NO3 (ammonium nitrate), 5 g N m−2) decreased CH4 emission up to 57% and 50% at low and high water tables, respectively, but correspondingly increased N2O emission by 2.5 and 10.4 times. Addition of S (Na2SO4 (sodium sulfate), 2.5 g S m−2) decreased CH4 and N2O emission by 64% and 79% at the low water table, respectively, but had a slightly positive effect at the high water table. These results indicated that the responses of CH4 and N2O emissions to the S deposition depend on the water table condition in the high-altitude peatland.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.346
Threshold uncertainty score0.521

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 studies0.0000.000
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 head0.004
GPT teacher head0.181
Teacher spread0.177 · 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 designBench or experimental
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

Citations39
Published2014
Admission routes1
Has abstractyes

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