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Record W2362988735

Review on coupling of interactive functions between carbon and nitrogen cycles in forest ecosystems

2006· article· en· W2362988735 on OpenAlexaff
Xiang Wen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicForest, Soil, and Plant Ecology in China
Canadian institutionsCAE (Canada)
Fundersnot available
KeywordsEcosystemEnvironmental scienceCarbon cycleAutotrophForest ecologyNitrogen cycleNutrient cycleProductivityNitrogenEcologyPlant litterCyclingSoil respirationChemistryBiologyForestryGeography
DOInot available

Abstract

fetched live from OpenAlex

There are many uncertainties in the role of forest ecosystems in slowing down the increases of atmospheric carbon dioxide concentration and their responses to and feedbacks on the global change associated with the increasing CO_2 concentration in the atmosphere and the global increase in nitrogen deposition.Research on the interactions and coupling of carbon and nitrogen cycling in forest ecosystems can pinpoint and thus reduce these uncertainties.Such studies also improve the understanding of the relationships between the productivity and nutrient cycling in forest ecosystems and mechanisms for sustaining the long-term site productivity.The coupling between carbon and nitrogen cycles in forest ecosystems includes photosynthetic processes,autotrophic respiration process,decomposition process of litter and soil organic matter,fine root turnover process,and heterotrophic respiration process.Both positive and negative feedback mechanisms and nonlinear relationships are involved in these processes,which ultimately determine the carbon balance in forest ecosystems.This paper reviews the current literature on the coupling functions between carbon and nitrogen cycles,and describes recent advances,existing shortcomings and limitations of research in this area. The directions for future research in this area are also discussed.

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 score0.208
Threshold uncertainty score0.968

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)0.0000.000

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 head0.022
GPT teacher head0.295
Teacher spread0.273 · 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
Published2006
Admission routes1
Has abstractyes

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