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Record W4406193140 · doi:10.1038/s44264-024-00045-x

Developing new varieties of deep-rooted crops: silicon and agroecosystem services

2025· article· en· W4406193140 on OpenAlexfundno aff
Zimin Li, Yunqiang Wang, Zhaoliang Song

Bibliographic record

Venuenpj Sustainable Agriculture · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSilicon Effects in Agriculture
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaUniversité LavalUniversité Catholique de Louvain
KeywordsAgroecosystemAgroforestryAgronomySiliconAgricultural engineeringBusinessEnvironmental scienceBiologyEngineeringAgricultureEcologyChemistry

Abstract

fetched live from OpenAlex

Agroecosystems, which cover nearly 40% of Earth’s land surface, are dominant drivers of Earth’s biogeochemical cycling and maintain the vital life-support systems to feed a growing global population via provisioning ecosystem services, such as food and fiber production 1 . However, agroecosystem sustainable development is under threat due to multiple stresses of environmental and climatic changes such as warming, pest and disease, saline, drought and acidic constraints, and biodiversity loss 2 , 3 . Moreover, state-of-the-art climate projections have predicted that the environmental and climatic changes would continuously increase the global intensification of heavy precipitation events and heat extremes and surfaces with stronger or longer-lasting droughts in agroecosystems 4 , 5 .

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.641
Threshold uncertainty score0.555

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.004
GPT teacher head0.202
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 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

Citations6
Published2025
Admission routes1
Has abstractyes

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