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Record W3194310144 · doi:10.3389/frym.2021.604096

Who Feeds The Plants? Microbes!

2021· article· en· W3194310144 on OpenAlexaff
Aline Lacaze, Antoine Zboralski, David L. Joly

Bibliographic record

VenueFrontiers for Young Minds · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Parasitism and Resistance
Canadian institutionsUniversité de Moncton
Fundersnot available
KeywordsAgricultureNutrientSustainable agricultureSoil nutrientsSoil bacteriaEnvironmental scienceGreen foodAgronomyBiotechnologyBiologyBacteriaAgroforestryEcology

Abstract

fetched live from OpenAlex

Plants get their food from the soil they grow in. But this food is only available thanks to a great diversity of microbes, especially bacteria and fungi, that can chemically and mechanically transform materials in the soil into nutrients. These soil microbes have received a lot of attention from scientists all around the world, because these organisms could improve the way we grow our food and could make agriculture more sustainable. In this article, we explore some of the main mechanisms used by these microbes to feed plants.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.139
Threshold uncertainty score0.252

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 head0.009
GPT teacher head0.195
Teacher spread0.186 · 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 designNot applicable
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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

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