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Record W4405480998 · doi:10.1002/ldr.5443

Microplastic Effects on Soil Aggregation in Sterilized and Non‐Sterilized Soils

2024· article· en· W4405480998 on OpenAlexaff
Haixiao Li, Longyuan Yang, Chenghui Luo, Le Liu, Cheng Li, Noura Ziadi

Bibliographic record

VenueLand Degradation and Development · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsAgriculture and Agri-Food Canada
FundersYoung Elite Scientists Sponsorship Program by TianjinHubei Polytechnic UniversityNational Natural Science Foundation of China
KeywordsMicroplasticsSoil waterEnvironmental chemistryEnvironmental scienceMicroorganismIncubationChemistrySoil scienceBiology

Abstract

fetched live from OpenAlex

ABSTRACT The adverse impact of soil microplastics on aggregation is generally attributed to the potential toxicity to soil microorganisms. However, there have been few studies that use sterile soil as a control environment for comparison with regular soils to test this hypothesis. Consequently, this study conducted soil incubation with oven‐heated sterilized soils to explore the effects of polyethylene (PE) and polypropylene (PP) microplastics (at sizes of 0.595, 0.089, and 0.009 mm) on the soil aggregate stabilities. The aim was to determine if their primary mode of action is through biogenic interactions with soil microorganisms. The microplastics reduced 49% and 82% water‐stable aggregates of 0.5–1 and 1–2 mm fractions in Tianjin soils, respectively. The effects of microplastics were particularly pronounced in the non‐sterilized soils during the initial month of incubation. Additionally, microplastics increased the surface roughness of aggregates by an average of 39%, yet microplastics did not significantly affect the aggregate mechanical stability in either soil type. This study suggests that interactions between soil microplastics and microorganisms work crucially on soil aggregation, but microplastics could also possibly affect soil aggregation through non‐biogenic pathways.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.878
Threshold uncertainty score0.409

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 head0.007
GPT teacher head0.205
Teacher spread0.198 · 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 designOther design
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

Citations1
Published2024
Admission routes1
Has abstractyes

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