Eco-Environmental Aspects of COVID-19 Pandemic and Potential Control Strategies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A new coronavirus-strain from a zoonotic reservoir (probably bat)-termed as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-has recently claimed more than two million deaths worldwide. Consequently, a burst of scientific reports on epidemiology, symptoms, and diagnosis came out. However, a comprehensive understanding of eco-environmental aspects that may contribute to coronavirus disease 2019 (COVID-19) spread is still missing, and we therefore aim to focus here on these aspects. In addition to human-human direct SARS-CoV-2 transmission, eco-environmental sources, such as air aerosols, different public use objects, hospital wastes, livestock/pet animals, municipal wastes, ventilation facilities, soil and groundwater potentially contribute to SARS-CoV-2 transmission. Further, high temperature and humidity were found to limit the spread of COVID-19. Although the COVID-19 pandemic led to decrease air and noise pollution during the period of lockdown, increased use of masks and gloves is threatening the environment by water and soil pollutions. COVID-19 badly impacted all the socio-economic groups in different capacities, where women, slum dwellers, and the people lacking social protections are the most vulnerable. Finally, sustainable strategies, waste management, biodiversity reclaim, eco-friendly lifestyle, improved health infrastructure and public awareness, were proposed to minimize the COVID-19 impact on our society and environment. These strategies will seemingly be equally effective against any future outbreak.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it