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Record W4392002821 · doi:10.1080/11956860.2024.2311824

Invasion by <i>Hyptis suaveolens</i> modifies the effects of altered rainfall variability on nutrient cycling across seasons in a dry tropical grassland experiment

2023· article· en· W4392002821 on OpenAlexvenueno aff
Talat Afreen, Prakash Rajak, Hema Singh

Bibliographic record

VenueEcoscience · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsMineralization (soil science)EcosystemEnvironmental scienceGrasslandNutrient cycleCyclingNutrientAgronomyEcologySoil waterSoil scienceBiologyForestryGeography

Abstract

fetched live from OpenAlex

Ecosystems often recover rapidly when changes in climatic conditions are moderate, but extreme changes may push the ecosystem beyond its biological threshold, resulting in rather profound changes in its functioning and species composition. We experimentally evaluated how the ecosystem functioning of tropical grassland may change under changing precipitation variability by investigating shifts in soil properties and their relation to plant invasions. We found that soil moisture, soil pH, inorganic N content (NO3 - N + NH4 - N), N mineralization rate, and soil CO2 flux increase with a rise in rainfall. Moreover, the grassland plots invaded by Hyptis suaveolens, particularly those with increased precipitation, demonstrated elevated mineralization rates, substantial nutrient accumulation, and a reduced microbial biomass in comparison to the uninvaded plots. Our study highlighted that, following soil moisture (SM) and soil temperature (ST), N mineralization emerged as the third primary driver of soil CO2 flux. Enhanced precipitation led to increased N mineralization and subsequent CO2 emissions. The results indicate that escalated CO2 flux in invaded plots could be linked to invasive H. suaveolens adverse effects on soil processes, potentially leading to short-term inefficient nutrient cycling and elevated CO2 emissions, with potential consequences for the overall stability of the ecosystem.

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)0.000
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 score0.621
Threshold uncertainty score0.187

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.014
GPT teacher head0.246
Teacher spread0.232 · 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
Published2023
Admission routes1
Has abstractyes

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