The controversies of silicon's role in plant biology
Bibliographic record
Abstract
Contents Summary 67 I. Introduction 68 II. Silicon transport in plants: to absorb or not to absorb 69 III. The role of silicon in plants: not just a matter of semantics 71 IV. Silicon and biotic stress: beyond mechanical barriers and defense priming 76 V. Silicon and abiotic stress: a proliferation of proposed mechanisms 78 VI. The apoplastic obstruction hypothesis: a working model 79 VII. Perspectives and conclusions 80 Acknowledgements 81 References 81 SUMMARY: Silicon (Si) is not classified as an essential plant nutrient, and yet numerous reports have shown its beneficial effects in a variety of species and environmental circumstances. This has created much confusion in the scientific community with respect to its biological roles. Here, we link molecular and phenotypic data to better classify Si transport, and critically summarize the current state of understanding of the roles of Si in higher plants. We argue that much of the empirical evidence, in particular that derived from recent functional genomics, is at odds with many of the mechanistic assertions surrounding Si's role. In essence, these data do not support reports that Si affects a wide range of molecular-genetic, biochemical and physiological processes. A major reinterpretation of Si's role is therefore needed, which is critical to guide future studies and inform agricultural practice. We propose a working model, which we term the 'apoplastic obstruction hypothesis', which attempts to unify the various observations on Si's beneficial influences on plant growth and yield. This model argues for a fundamental role of Si as an extracellular prophylactic agent against biotic and abiotic stresses (as opposed to an active cellular agent), with important cascading effects on plant form and function.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".