Biomass, Protein Content and Cell Damage in Tanzania Grass Irrigated With Saline Water
Bibliographic record
Abstract
The scarcity of good water quality in semiarid region, combined with the high cost of pumping, has been the main limiting factor for increasing the irrigated area. The use of saline water for irrigation is a very common in semiarid zones, which can result in the soil salinization if irrigation management is not appropriated. To evaluate the biomass production, biochemical components and water consumption of Tanzania grass (Panicum maximum) irrigated with water salinity (1.5, 3.0, 4.5 and 6.0 dS m-1) an experiment in greenhouse was carried out in the Universidade Federal Rural do Semi-Árido, Mossoró, Rio Grande do Norte State, Brazil. The variables dry matter, crude protein, ashes, cellular damage on leaves and consume water in the first and second cut of the grass were analyzed under completely randomized design with five treatments and six replications. Salinity water up to 6.0 dS m-1 can be used for irrigation of Tanzania grass plants, with small yield losses. Increased salinity reduces water consumption and increases the water use efficiency of Tanzania grass. Tanzania grass plants have increased protein content when subjected to saline stress, which is a mechanism of action to osmotic adjustment and allows the reduction of plant leaf damage in the second cycle.
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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".