Elucidating Anthropogenic Impacts On Water Quality Through Spatial Heterogeneity In The Laurentian Great Lakes And The Global Fast Fashion Industry
Bibliographic record
Abstract
Freshwater resources are vital for human survival including for consumption, transportation, and production of goods. We explore the impact of anthropogenic activity on water quality through: 1) the spatial heterogeneity in the northern nearshore regions of the Canadian Great Lakes and 2) the state of fast fashion in the top garment producing countries: China, Bangladesh, Vietnam, Turkey, India, and Indonesia. Water quality conditions varied spatially from oligotrophic conditions in Lake Superior to eutrophic conditions with elevated concentrations of nutrients and chlorophyll-a in Lake Erie. Further, we found that policy, sustainability and wastewater are key research areas in the top garment-producing countries. However, there was variation in the number of publications, terminology, knowledge gaps, and barriers that impede more sustainable garment production in the top garment-producing countries. We suggest investments in management and education to protect and use our freshwater resources more sustainably.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| 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".