MétaCan
Menu
Back to cohort
Record W7135092006 · doi:10.5376/ijmec.2025.15.0027

Nutrient Cycling and Decomposition Processes in Grassland Ecosystems

2025· article· W7135092006 on OpenAlexvenueno aff
Jiong Fu

Bibliographic record

VenueInternational Journal of Molecular Ecology and Conservation · 2025
Typearticle
Language
FieldSocial Sciences
TopicForest, Soil, and Plant Ecology in China
Canadian institutionsnot available
Fundersnot available
KeywordsNutrient cycleBiogeochemical cycleGrasslandEcosystemNutrientPlant litterSoil carbonCarbon cycleTerrestrial ecosystem

Abstract

fetched live from OpenAlex

This study analyzed the cycling mechanisms of major nutrients in grasslands, the decomposition processes of litter and soil organic matter, the ecological functions of microorganisms and soil animals, as well as the regulatory effects of climate change and human interference on nutrient cycling. Research has found that grassland ecosystems, with their unique vegetation structure, climatic conditions and soil environment, play a significant role in the global biogeochemical cycle. As one of the largest types of terrestrial ecosystems in terms of area, grasslands undertake key functions such as carbon storage, soil conservation, energy flow and food supply, while the nutrient cycle and decomposition process constitute the core mechanism for their stable operation. In the grassland, plants, microorganisms and soil animals achieve the redistribution of key nutrients such as nitrogen, phosphorus and carbon through multi-scale and multi-pathway interactions. Meanwhile, the decomposition of litter, rhizosphere processes and the physical and chemical environment of the soil jointly regulate the speed and direction of nutrient release, thereby maintaining grassland productivity and system resilience. This research is of great significance for understanding the sustainable state of grassland ecosystems, predicting future functional changes and formulating management strategies, providing a theoretical basis for grassland protection and ecological management.

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)0.001
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 score0.116
Threshold uncertainty score0.579

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.008
GPT teacher head0.299
Teacher spread0.291 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Molecular Ecology and ConservationSame topicForest, Soil, and Plant Ecology in ChinaFrench-language works237,207