Impact of Two Hydrophilic Acrylic-Based Polymers on the Physical Properties of Three Substrates and the Growth of Petunia ×hybrida `Brilliant Pink'
Bibliographic record
Abstract
Hydrophilic polymers or hydrogels have shown potential to increase water retention of media and to reduce irrigation frequency. This property would be particularly useful in the production of fast growing species in which large amounts of water are needed. This study evaluated the effect of two acrylic-based hydrogels on water desorption curve and hydraulic conductivity of substrates and on plant growth. The duration of their effects was also investigated. Rooted cuttings of Surfinia (Petunia ×hybrida `Brilliant Pink') were transplanted into 30-cm pots containing one of three different substrates amended with one of two types of hydrogels, a commercial acrylic polymer, and a commercial acrylic-acrylamide copolymer, and grown for 9 weeks under well watered conditions and then imposed with a drought. Results indicated that both polymer types gave similar results. The substrates' physical properties (air-filled porosity, available water) at potting time were significantly affected by hydrogel addition, but differences vanished within 9 weeks of growth. Hydrogels had no significant effect on the point at which plant wilted and on the substrate's unsaturated hydraulic conductivity. Shoot dry weight was affected by substrate and hydrogel and was positively correlated to water content between container capacity and -10 kPa of water potential, or between container capacity and the soil water potential at plant turgor loss.
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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 | 0.000 |
| 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".