Quantification and evaluation of chemical footprint with four methods:A case of the dyeing and printing process of a polyester dress
Bibliographic record
Abstract
The textile printing and dyeing industry, with huge chemical demand, has a negative impact on the ecosystem. Chemical footprint quantifies the toxic impacts of chemical pollutants by assessing their behaviour in the environment. In this paper, four methods were used to calculate and evaluate the chemical footprint of a polyester dress printing and dyeing process. The chemical footprint of the printing and dyeing process of a polyester dress, calculated with USEtox, Assessment of Mean Impact, Score System, and Strategy Tool, was 1585.51 PAF×m3 ×day, 14089.04 l, 331, and 75, respectively. Scouring, colouring, pretreatment, and printing were identified as the major procedures contributing, with the antifoaming agents and the chelating disperse agents as the major auxiliaries contributing. The results of the Strategy Tool are limited in their representativeness of environmental load. Compared to other methods, AMI ensures that the evaluation results are scientific while maintaining user-friendliness.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".