Transpiration-dependent passive silica accumulation in cucumber (<i>Cucumis sativus</i>) under varying soil silicon availability
Bibliographic record
Abstract
Regulation of the uptake of silicon (Si) varies among plant species; some species may passively transport Si, through transpiration, from soils to shoots, while others actively transport silica and deposit it in leaf tissues at high concentrations. Cucumber ( Cucumis sativus L.) accumulates moderate amounts of silica in leaves, but the relative importance of passive and active processes pertaining to silica accumulation is poorly understood. In a factorial experiment with cucumber seedlings, we manipulated transpiration rates by changing humidity and air movements around pot-grown plants receiving a daily supply of solutions containing 0, 1, 1.5, and 2 mmol Si·L–1. Higher transpiration rates resulted in significantly greater Si per unit leaf mass after 4 days, suggesting that passive processes affect the rate of silica accumulation. Actual silica accumulation during the 4-day period was significantly higher than the expected accumulation attributable to passive transport alone in 1 and 1.5 mmol Si·L–1 treatments, while passive processes alone could account for the actual silica accumulation at 2 mmol Si·L–1. We conclude that the relative importance of active and passive processes in silica deposition in cucumber leaves depends on transpiration rates and the balance between soil Si availability and plants’ demands for Si.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".