Decoding spatiotemporal dynamics of post-consumer textile waste generation and management using ternary plots
Bibliographic record
Abstract
This study aims to understand the post-consumer textile waste (PCTW) management dynamics of a southeastern US state from 2014 to 2022 using time-series ternary diagrams. Multiple linear regression models were developed to assess the impact of various factors on PCTW generation and management practices. During the period, the study revealed a 33% increase in PCTW generation, averaging 40 kg per capita in 2022, with significant variability influenced by demographic and socioeconomic factors. A shift towards PCTW recycling and reuse across regional levels are observed, probably due to advanced waste sorting systems and improved recycling program accessibility. Population density, land area, household structure, and education were significant predictors for the predictive models (p < 0.05, and 0.39 < R 2 < 0.97) of PCTW generation, landfilling, recycling, and reuse. Recycling was preferred over landfilling more often by individuals with higher education levels, with the lowest disposal rate at 27%. Smaller household sizes favored reuse and donation, underscoring the need for custom PCTW management strategies. Higher recycling rates, reaching up to 74.2% are found in households with fewer females, and in areas with less employers. The proposed visualization framework helps to facilitate development of evidence-based waste policies for a sustainable PCTW management in diverse regional contexts.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".