Ascophyllum nodosum Biostimulant Improves the Growth of Zea mays Grown Under Phosphorus Impoverished Conditions
Bibliographic record
Abstract
Phosphorous is one of the major limiting factors determining plant growth. Current agricultural practices mainly rely on the use of chemical fertilizers posing threat to the ecosystem. In this study, the application of an Ascophyllum nodosum extract (ANE) in phosphorous (P)-limited conditions improved the fresh and dry weight of shoots and roots of Zea mays. ANE-treated Z. mays grown under P-limited conditions showed a higher P content than the control. ANE activated simultaneous responses, at multiple levels, in Z. mays grown under P-limited conditions as seen from the regulation of gene expression at the whole-plant level to specific biochemical responses on a subcellular level. ANE-supplemented Z. mays grown under P-limited conditions also showed reduced electrolyte leakage and lipid peroxidation by an improved membrane stability. ANE treatment reduced P-limitation-induced oxidative damage in Z. mays by reducing H2O2 and O2- accumulation. Furthermore, ANE also induced the accumulation of the total contents of soluble sugars, amino acids, phenolics, and flavonoids. Gene expression analysis suggested that ANE differentially modulated the expression of P-starvation responsive genes involved in metabolic, signal transduction, and developmental pathways in Z. mays. ANE also modulated the expression of genes involved in sugar, lipid, and secondary metabolism. Thus, this study illustrated the role of ANE in improving the productivity of Z. mays, an important crop, in P-limited conditions. Furthermore, it sets the framework to increase agricultural productivity in nutrient deficient soils using a sustainable, eco-friendly strategy.
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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) | 0.000 | 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".