Molecular mechanisms underpinning phosphorus‐use efficiency in rice
Bibliographic record
Abstract
, Pi) is an essential macronutrient integral to energy metabolism as well as a component of membrane lipids, nucleic acids, including ribosomal RNA, and therefore essential for protein synthesis. The Pi concentration in the solution of most soils worldwide is usually far too low for maximum growth of crops, including rice. This has prompted the massive use of inefficient, polluting, and nonrenewable phosphorus (P) fertilizers in agriculture. We urgently need alternative and more sustainable approaches to decrease agriculture's dependence on Pi fertilizers. These include manipulating crops by (a) enhancing the ability of their roots to acquire limiting Pi from the soil (i.e. increased P-acquisition efficiency) and/or (b) increasing the total biomass/yield produced per molecule of Pi acquired from the soil (i.e. increased P-use efficiency). Improved P-use efficiency may be achieved by producing high-yielding plants with lower P concentrations or by improving the remobilization of acquired P within the plant so as to maximize growth and biomass allocation to developing organs. Membrane lipid remodelling coupled with hydrolysis of RNA and smaller P-esters in senescing organs fuels P remobilization in rice, the world's most important cereal crop.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".