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Record W2805704833 · doi:10.1007/s11676-018-0721-7

Nitrous oxide emissions from three temperate forest types in the Qinling Mountains, China

2018· article· en· W2805704833 on OpenAlexaff
Wei Xue, Changhui Peng, Huai Chen, Hui Wang, Qiuan Zhu, Yanzheng Yang, Junjun Zhang, Wanqin Yang

Bibliographic record

VenueJournal of Forestry Research · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsUniversité du Québec à Montréal
FundersSpecialized Research Fund for the Doctoral Program of Higher Education of China
KeywordsEnvironmental scienceTemperate climateTemperate forestNitrous oxideTemperate rainforestTemperate deciduous forestPinus tabulaeformisFlux (metallurgy)Atmospheric sciencesForestryHydrology (agriculture)EcologyEcosystemGeographyDeciduousGeologyBotanyChemistryBiology

Abstract

fetched live from OpenAlex

To understand soil N 2 O fluxes from temperate forests in a climate-sensitive transitional zone, N 2 O emissions from three temperate forest types ( Pinus tabulaeformis , PTT; Pinus armandii , PAT; and Quercus aliena var. acuteserrata , QAT) were monitored using the static closed-chamber method from June 2013 to May 2015 in the Huoditang Forest region of the Qinling Mountains, China. The results showed that these three forest types acted as N 2 O sources, releasing a mean combined level of 1.35 ± 0.56 kg N 2 O ha −1 a −1 , ranging from 0.98 ± 0.37 kg N 2 O ha −1 a −1 in PAT to 1.67 ± 0.41 kg N 2 O ha −1 a −1 in QAT. N 2 O emission fluctuated seasonally, with highest levels during the summer for all three forest types. N 2 O flux had a significantly positive correlation with soil temperature at a depth of 5 cm or in the water-filled pore space, where the correlation was stronger for temperature than for the water-filled pore space. N 2 O flux was positively correlated with available soil nitrogen in QAT and PAT. Our results indicate that N 2 O flux is mainly controlled by soil temperature in the temperate forest in the Qinling Mountains.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.031
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.058
GPT teacher head0.326
Teacher spread0.268 · 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
Published2018
Admission routes1
Has abstractyes

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