Study on the Effect of Natural dyes and Synthetic dyes on Textile Fabric
Bibliographic record
Abstract
Natural dyes are very useful for dyeing textile fibre. Natural dyes have been used for long back. In the Mughal regime, it was found that natural dyes were used to dye textile materials. Starting from the Muslin to the household textiles the use of natural dyes was significant. For more than a hundred years, it was evident that synthetic dyes become popular for dyeing textile material. Natural dyes for jamdani sarees are not easily available in Bangladesh. These dyes are imported from outside of Bangladesh and for this reason, this type of dyes is not available in the local market. But at the same time, the risk of using synthetic dyes is high for human health because of cause different health hazards like breathing problems, skin diseases and skin cancer. Compared to synthetic dyes natural dyes are not easily available and they are costly at the same time. The unavailability of natural dyes made the use of synthetic dyes more popular among the manufacturers of textile products. Though natural dyes are sustainable, due to unavailability and high cost it is hindering the acceptance to use natural dyes. In this study, it was evident that natural dyes normally have significant eco-friendly properties especially the absence of banned amines compared to synthetic dyes. It was found that synthetic dyes have banned amines.
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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 | 0.001 |
| 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) | 0.003 | 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".