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Record W2371744470

Interactions between terrestrial ecosystem water and carbon cycles and their simulation methods:A review

2009· article· en· W2371744470 on OpenAlexaff
Ren Liliang

Bibliographic record

VenueShengtaixue zazhi · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEnvironmental scienceCarbon cycleTerrestrial ecosystemEcosystemWater cycleSoil carbonSoil respirationClimate changeSoil waterEcologyAtmospheric sciencesSoil scienceBiologyGeology
DOInot available

Abstract

fetched live from OpenAlex

This paper reviewed the researches on the interactions between water and carbon cycles in terrestrial ecosystems,and the algorithms developed to simulate these cycles and their interactions. Future research efforts to be taken were also suggested. Carbon and water cycles are the coupled ecological processes in terrestrial ecosystems. They themselves and their interactions are affected by climate,atmospheric composition,and human activities,and impose significant feedbacks on climate system,being the research focuses in global change study. Many observational and modeling studies have been conducted to study the interactions of the two cycles at various spatial and temporal scales as well as their responses to the changes in environmental factors and land cover. Soil water markedly affects the main components of the carbon cycle (photosynthesis and respiration),but the affecting strength varies with the types of ecosystems. To accurately simulate soil water dynamics and its roles in the carbon cycle is the basis of reliable simulation of terrestrial carbon budget. Efforts should be taken to implement coupled modeling of carbon and water cycles in ecological and hydrological models. Most of current models ignore the effects of topography on the horizontal redistribution of soil water,and utilize empirical methods to simulate the effects of soil water on heterotrophic respiration,which limit the reliability of carbon budget estimation and needs to be resolved.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.763
Threshold uncertainty score0.445

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 head0.070
GPT teacher head0.333
Teacher spread0.263 · 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

Citations4
Published2009
Admission routes1
Has abstractyes

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