Bibliographic record
Abstract
Role of Silicon in PlantsSilicon (Si), the second most abundant element on earth surface, is rapidly gaining attention in agriculture because of its many beneficial effects for plants.Hundreds of studies performed with several plant species and under diverse growth conditions have demonstrated the favorable benefits of Si fertilization, particularly in alleviating biotic and abiotic stresses (Fauteux et al., 2005(Fauteux et al., , 2006)).Ever since the breakthrough discovery of genes explaining the molecular mechanisms of Si uptake and transport in plants a decade ago (Ma et al., 2006(Ma et al., , 2007)), many research endeavors have tried to explain how and why Si presence in plants confers advantages.The most challenging aspect consists in defining a mechanistic model explaining the precise mechanisms involved in Si-derived stress tolerance.While many hypotheses have been proposed, there is no conclusive evidence showing exactly how Si plays a role in stress tolerance.Current efforts to resolve this enigma involve comprehensive analyses of the effect of Si supplementation on various abiotic and biotic stresses, biochemical and physiological parameters, mineral co-localization and distribution, and transcriptomic and metabolomic responses.At the same time, research activities are focused on improving Si fertilization and Si sources for crop cultivation.The present research topic compiles many aspects helpful to generate a better understanding required for the optimal utilization of Si to promote sustainable development and climate-adapted cropping.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.026 | 0.018 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".