Positive and Negative Effects of COVID-19 Pandemic on Aquatic Environment: A Review
Bibliographic record
Abstract
In December 2019, a novel coronavirus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak was reported for the first time in Wuhan, Hubei province, China. This coronavirus has been referred as Coronavirus Disease 2019 or COVID-19 by World Health Organization (WHO). The spread of COVID-19 has become unstoppable, infecting around 93.5 million people worldwide, with the infections and deaths still increasing. Today, the entire planet has changed due to the greatest threat on the planet since the introduction of this lethal disease. This pandemic has left the world in turmoil and various measures have been taken by many countries including movement control order or lockdown, to slow down or mitigate the infection. Since the lockdown has been implemented almost in all affected countries, there has been a significant reduction in anthropogenic activity, including a reduction in industrial operations, vehicle numbers, and marine-related activities. All of these changes have also led to some unexpected environmental consequences. As a result of this lockdown, it had a positive and negative impact on the environment including the aquatic environment. Hence this review will therefore focus on the good and bad perspectives of the lockdown toward the aquatic environment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".