Changes in the Water Footprint during COVID-19 at Santa Rosa Hospital Located In Metropolitan Lima City, Peru
Bibliographic record
Abstract
This study aims to investigate the water footprint of Santa Rosa Hospital, situated in the city of Lima, Peru.The analysis period covers the years 2015 to 2019, before the onset of the COVID-19 pandemic, and the period from 2020 to 2022, after the COVID-19 pandemic.The primary objective is to understand how the health crisis has impacted water consumption and to identify measures taken to optimize its usage.The methodology involves the calculation of both blue and grey water footprints, with the green footprint excluded due to the absence of green areas in the hospital premises.Data essential for determining the water footprint were sourced from technical reports published by public scientific entities.The findings revealed an increase in the water footprint following the onset of COVID-19.A management study implemented significant savings for the year 2022.Through this analysis, the hospital aims to reinforce its commitment to sustainable water management, contributing to a healthier and more environmentally sustainable future.
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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 | 0.001 | 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".