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Record W4411327635 · doi:10.3390/land14061281

The Impact of Different Almond Orchard Management Practices in Hyper-Arid Ecosystems on Soil Microbial Communities

2025· article· en· W4411327635 on OpenAlexaff
Itaii Applebaum, Gil Eshel, Tirza Doniger, Yosef Steinberger

Bibliographic record

VenueLand · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Parasitism and Resistance
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsOrchardAridEcosystemAgroforestryEcosystem servicesEnvironmental scienceGeographyEcologyEnvironmental resource managementBiology

Abstract

fetched live from OpenAlex

The use of service (cover) crops is widely practiced in soil agriculture due to their many benefits, including enhanced nutrient supply and improved soil health. Bacteria, as major decomposers of plant residues in the soil, play essential roles in nutrient cycling. This study examined the impact of various almond orchard management practices on the soil microbial community composition in a hyper-arid ecosystem. High-throughput sequencing was used to compare the microbial communities in two adjacent almond orchards managed with either organic (ORG) or regenerative agriculture (RA) practices, alongside an uncultivated (UC) site. Notably, little is known about the responses of soil bacterial communities in hyper-arid regions to intercrop mulch from service crops. This study may offer insights into the ecological limits of the benefits of service crops in promoting soil health under extreme conditions. Our findings demonstrate that RA management can alter soil organic carbon levels and reshape microbial communities by increasing overall bacterial abundance and enriching specific keystone taxa. These changes may have significant implications for nutrient cycling processes in hyper-arid agroecosystems.

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 score0.109
Threshold uncertainty score0.931

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 head0.020
GPT teacher head0.258
Teacher spread0.238 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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