Social Life Cycle Assessment of Mexico City's Water Cycle
Bibliographic record
Abstract
Abstract There is a need to generate micro‐scale indicators to measure progress towards meeting Sustainable Development Goals (SDGs) 6 and 8. In this sense, this study applies Social Life Cycle Assessment, including Quality of Employment (QoE) and Adequate Public Water Supply (AWS) indicators to assess the social performance of Mexico City's water cycle to identify the level (low, medium and high) of the potential risk of social impact (PR). The results show that the labor hours (WH) required by 1m 3 of water in Mexico City is equivalent to 0.062 WH. The QoE indicator shows that 94% of WH are associated with high PR due to low wages. For AWS, 5% is associated with a high PR for local communities in the Cutzamala system due to poor water quality and consumption of bottled water. This case study demonstrates that progress on QoE and AWS indicators can significantly contribute to achieving SDGs 6 and 8 in the water management of communities involved in Mexico City's water cycle.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 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".