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Record W4283696731 · doi:10.1128/msystems.00247-22

A Drying-Rewetting Cycle Imposes More Important Shifts on Soil Microbial Communities than Does Reduced Precipitation

2022· article· en· W4283696731 on OpenAlexafffund
Xiaobo Wang, Hamed Azarbad, Laura Leclerc, Jessica Dozois, Eugenie Mukula, Étienne Yergeau

Bibliographic record

VenuemSystems · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil Moisture and Remote Sensing
Canadian institutionsInstitut National de la Recherche Scientifique
FundersNational Key Research and Development Program of ChinaFonds de recherche du Québec – Nature et technologiesFonds de recherche du QuébecLanzhou University
KeywordsPrecipitationEnvironmental scienceEnvironmental chemistryPulp and paper industryAgronomyChemistryBiologyMeteorologyGeographyEngineering

Abstract

fetched live from OpenAlex

Climate change will have a profound effect on the precipitation patterns of global terrestrial ecosystems. Seasonal and interannual uneven distributions of precipitation will lead to increasing frequencies and intensities of extreme drought and rainfall events, which will affect crop productivity and nutrient contents in various agroecosystems. However, we still lack knowledge about the responses of soil microbial communities to reduced precipitation and drying-rewetting events in agroecosystems. Our results demonstrated an uneven response of the soil microbiome and a dramatic shift in microbial community diversity and structure to a significant drying-rewetting event with a large increase in the relative abundance of archaeal ammonia oxidizers. These findings highlight the larger importance of rewetting of dry soils on microbial communities, as compared to decreased precipitation, with potential for changes in the soil nitrogen cycling.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.222
Teacher spread0.213 · 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 source (direct Gemma or distilled Codex), 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

Citations42
Published2022
Admission routes2
Has abstractyes

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