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Record W4386945336 · doi:10.1111/nph.19269

The potential of arbuscular mycorrhizal fungi to enhance metallic micronutrient uptake and mitigate food contamination in agriculture: prospects and challenges

2023· article· en· W4386945336 on OpenAlexafffund
Eduardo Moreno‐Jiménez, Nuria Ferrol, Nicolas Corradi, Jesús M. Peñalosa, Matthias C. Rillig

Bibliographic record

VenueNew Phytologist · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsUniversity of Ottawa
FundersAgencia Estatal de InvestigaciónEuropean Regional Development FundEuropean CommissionNatural Sciences and Engineering Research Council of CanadaAlexander von Humboldt-StiftungMinisterio de Ciencia e InnovaciónUniversity of Ottawa
KeywordsMicronutrientArbuscular mycorrhizal fungiContaminationAgricultureEnvironmental scienceMycorrhizal fungiBiologyAgronomyAgroforestryBiotechnologyEnvironmental chemistryEcologyChemistryHorticultureInoculation

Abstract

fetched live from OpenAlex

Optimizing agroecosystems and crops for micronutrient uptake while reducing issues with inorganic contaminants (metal(loid)s) is a challenging task. One promising approach is to use arbuscular mycorrhizal fungi (AMF) and investigate the physiological, molecular and epigenetic changes that occur in their presence and that lead to changes in plant metal(loid) concentration (biofortification of micronutrients or mitigation of contaminants). Moreover, it is important to understand these mechanisms in the context of the soil microbiome, particularly those interactions of AMF with other soil microbes that can further shape crop nutrition. To address these challenges, a two-pronged approach is recommended: exploring molecular mechanisms and investigating microbiome management and engineering. Combining both approaches can lead to benefits in human health by balancing nutrition and contamination caused by metal(loid)s in the agro-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)0.000
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 score0.848
Threshold uncertainty score0.425

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 head0.013
GPT teacher head0.230
Teacher spread0.217 · 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

Citations38
Published2023
Admission routes2
Has abstractyes

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