Impact of textile sludge on the growth of red amaranth (Amaranthus gangeticus)
Bibliographic record
Abstract
In Bangladesh, the sludge of textile effluent treatment plant has been considered as a potential environmental threat due to its huge volume and chemical content. Thus, the present study was carried out to assess the reuse possibility/potentiality of textile sludge in agricultural applications. Textile sludge was applied at different loading ratios (0–100 % sludge) with soil for the pot cultivation of red amaranth ( Amaranthus gangeticus ); subsequently, chemical analyses were carried out on the harvested plants. The results showed that the content of plant nutrients nitrogen (N), phosphorus (P), potassium (K) and iron (Fe) in sludge was significant compared to organic manure along with a high content of total organic carbon (TOC). The growth parameters (height, number of leaves, leaf area and root length) of red amaranth were affected by the application of textile sludge. Maximum plant growth was observed in the 100 % sludge treatment group, maybe because of the high content of plant nutrients. However, the root length and number of leaves were not significantly affected by the sludge. The plant analyses implied that addition of textile sludge did not increase the content of copper (Cu), cobalt (Co), cadmium (Cd), nickel (Ni) and manganese (Mn), but lead (Pb), chromium (Cr), zinc (Zn) and iron (Fe) content crossed the maximum permissible limit set by FAO/WHO. Textile sludge can improve the nutrient contents of pot soil and growth of red amaranth, which is revealed by pot experiments. Therefore, it can be used as soil improver if Pb, Cr, Zn and Fe content can be controlled in the textile sludge.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 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 teacher head, 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".