Commercial versus synthesized polymers for soil erosion control and growth of Chinese cabbage
Bibliographic record
Abstract
Soil erosion leads to environmental degradation and reduces soil productivity. The use of anionic polyacrylamide (PAM) and synthesized biopolymer (BP) using lignin, corn starch, acrylamide, and acrylic acid were tested to evaluate soil erosion, water quality, and growth of Chinese cabbage (Brassica campestris L.). Each treatment of PAM and BP was applied at 200 kg ha(-1) to loamy sand soil and subjected to a slope of 36% with a 20 mm h(-1) simulated rainfall. Application of BP decreased soil pH compared to the untreated check (CK); however, the soil pH was not altered with PAM. The decrease in pH might most likely be due to availability of anionic sites to be protonated on soils having pH >6 and soil buffering capacity. Both PAM and BP applications may not induce eutrophication with stable levels of total contents of N and P. With PAM and BP, the average values of suspended soil (SS) and turbidity were reduced by up to 96.0 and 99.9%, respectively, compared to CK. Reduction of SS can be attributed to increasing soil stability and shear strength by clay flocculation. There was no toxicity effects resulting from germination tests and the dry weight was increased by 17.7% (vs. CK) when PAM and BP were applied. These results are attributed to increases in water retention and plant-available water. The use of polymeric soil amendments is an environmentally friendly way to mitigate soil erosion and nonpoint source pollution.
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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".