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Record W4403514907 · doi:10.5376/msb.2024.15.0005

Dung Decomposers: Impact on Soil Fertility and Plant Growth

2024· article· en· W4403514907 on OpenAlexvenueno aff
Kaiwen Liang

Bibliographic record

VenueMolecular Soil Biology · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicSeedling growth and survival studies
Canadian institutionsnot available
Fundersnot available
KeywordsDecomposerSoil fertilityPlant growthEnvironmental scienceAgronomyFertilityAgroforestryEcologyBiologyEcosystemSoil water

Abstract

fetched live from OpenAlex

The findings revealed that dung decomposers significantly enhance soil nutrient content, including nitrogen, phosphorus, potassium, magnesium, and calcium, while reducing soil density, pH, and electrical conductivity. The presence of dung decomposers also led to increased plant growth parameters such as total leaf sugar, vitamin C, polyphenols, total protein, and amino acids. Additionally, dung decomposers improved the net photosynthetic rate, stomatal conductance, and chlorophyll content in plants. The study also highlighted the species-specific effects of dung decomposers on nutrient cycling and soil fertility, emphasizing the importance of maintaining beetle diversity to maximize soil health benefits. The application of dung decomposers as a soil amendment significantly enhances soil fertility and promotes plant growth. This sustainable strategy can improve crop yields and nutrient status, making it a viable option for organic agricultural practices. The study underscores the critical role of dung decomposers in nutrient cycling and soil health, particularly in dryland environments where nutrient losses due to grazing are prevalent.

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 score0.278
Threshold uncertainty score0.460

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 head0.008
GPT teacher head0.249
Teacher spread0.240 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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