Water Absorption by Hydrogel Using Fertilizers
Bibliographic record
Abstract
It was sought, in this research, to evaluate the effects of fertilizer solutions on water retention by hydrogel as well as if the application methods and fertilizers affect water retention when the hydrogel is added to the soil. In laboratory works (experiment 1), the completely randomized design was used taking into account four treatments: distilled water; Urea (UR – 2.0 g L-1 ) and Magnesium Sulfate + Monoammonium Phosphate (MS + MAP – 2.0 g L-1 each). Regarding the experiment 2, considering soil columns, a 2x3x2 factorial was used, corresponding to two hydrogel application methods: dry and mixed to the soil or diluted in water and concentrated in the center of the column; three fertilizer solutions: distilled water, UR (2.0 g L-1 ) and MS + MAP (2.0 g L-1 each) with two cycles and three repetitions. An additional witness (hydrogel free) was also added. Total water volume absorbed by hydrogel, volume stored in the soil after each cycle, pH and electrical conductivity (EC) of leached solutions were all analyzed. The MS solution was the one who impacted the most the water absorption by the hydrogel, principally when the hydrogel was not present in the soil. The pH and EC of leached solutions evidenced the fertilizer solutions salinity.
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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".