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Record W3082305772 · doi:10.1126/science.abc0393

Plants sustain the terrestrial silicon cycle during ecosystem retrogression

2020· article· en· W3082305772 on OpenAlexaff
Félix de Tombeur, Benjamin L. Turner, Étienne Laliberté, Hans Lambers, Grégory Mahy, Michel‐Pierre Faucon, Graham Zemunik, Jean‐Thomas Cornelis

Bibliographic record

VenueScience · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSilicon Effects in Agriculture
Canadian institutionsUniversité de Montréal
FundersFonds De La Recherche Scientifique - FNRS
KeywordsSiliconEcosystemTerrestrial ecosystemEnvironmental scienceTerrestrial plantAstrobiologyChemistryEcologyBiology

Abstract

fetched live from OpenAlex

The biogeochemical silicon cycle influences global primary productivity and carbon cycling, yet changes in silicon sources and cycling during long-term development of terrestrial ecosystems remain poorly understood. Here, we show that terrestrial silicon cycling shifts from pedological to biological control during long-term ecosystem development along 2-million-year soil chronosequences in Western Australia. Silicon availability is determined by pedogenic silicon in young soils and recycling of plant-derived silicon in old soils as pedogenic pools become depleted. Unlike concentrations of major nutrients, which decline markedly in strongly weathered soils, foliar silicon concentrations increase continuously as soils age. Our findings show that the retention of silicon by plants during ecosystem retrogression sustains its terrestrial cycling, suggesting important plant benefits associated with this element in nutrient-poor environments.

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 score0.005
Threshold uncertainty score0.010

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 communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.216
Teacher spread0.205 · 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

Citations116
Published2020
Admission routes1
Has abstractyes

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