Growth Efficacy of Sorghum and Rice Amended with Dried Versus Composted Aquatic Vegetation
Bibliographic record
Abstract
Aquatic vegetation is a potential source of organic matter and nutrients for crop production and soil sustainability. However, its high water content and presence of toxic compounds have been major deterrents for commercial application. This split-pot study evaluated the application of Pistia stratiotes (PS) (water lettuce) and Lyngbya wollei (LW) (filamentous cyanobacteria) to grow rice and sorghum. The aquatic vegetation was applied as dried and composted amendments on sandy (<3% organic matter) and muck (>80% organic matter) soils. A completely randomized split-pot design evaluated the effect of the amendments on root dry weight (RDW), shoot dry weight (SDW), and nutrient content of above ground biomass. The application of dried PS and LW on sandy soil produced larger and heavier sorghum shoots than those grown under composted treatments. Soil type was not a determinant factor of plant nutrient content: total Kjeldahl nitrogen, phosphorus, potassium and silicon. Shoot dry weight of rice grown on sandy soils was significantly greater than grown on muck soils using dried LW and composted LW treatments. The allelopathic effects of PS and LW were more pronounced on sandy soil compared to muck soil, indicating the potential application for using aquatic vegetation as a soil amendment on sandy soil in the future.
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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 | 0.000 |
| 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".