Bibliographic record
Abstract
Early efforts to vaccinate the Indian population were started on 16th January 2021. With this, a ray of hope came as people again starting their livelihoods, roads looked busy again, playgrounds were again full of children. Everything seems back to normal, while the Government was allowing all other activities with the option to follow the COVID appropriate behavior (CAB) keeping a blind eye to whether or not someone follows it. The immediate consequence of this laxity was that people were not following the CAB in particular, and by the end of January 2021, the situation was back to normal, as if there were no pandemics anywhere. While the rate of vaccination was slowly taking pace, the majority population believed that the vaccination may be necessary later, leading to the vaccine hesitancy. The second wave which started in the last quarter of March 2021 and spreader much faster than the first wave, is believed to be fueled by the additional strains of the coronavirus, as stated by many health experts. New coronavirus strains are thought to be more infectious home ground variants found in 61% of samples of genomes sequenced in many states in India. Even such news was reported by media rapidly, the laxity in the CAB and preventive measures, coupled with the presence of new variants, has resulted in a nationwide crisis. What caught the attention of the globe was despite the ongoing pandemic, the Indian Government allowed State assembly elections in the Eastern part of the country, which could have been delayed at this point. The prime Minster led Government faced this Critic from the opposition while the “Maha Kumbh Mela” organized at Haridwar attended by lacs of devotees who believed to bathe in a ritual river to pure themselves for their mistakes of past was also allowed by the Government. It was reported by media that about 7 million devotees attended the event and 1700 tested positive for covid-19 over 5 days period because no such social distancing measures or masks were used during the Maha Kumbh Mela by the attendees. Most of the public health agencies tried their best to dispel the myths and supported the campaigns associated with covid-19 vaccines but turned a blind eye and acted as a muted spectator for the election rallies and Maha Kumbh Mela. Following COB during election rallies and Maha Kumbh Mela will never go easy hand in hand, so the best is to follow the no man’s rule. What we believe the public health professionals of the country followed in deep agony and pressure. As India is engulfed in the second wave of covid-19, the current situation is deteriorated by the presence of counterfeit drugs, lack of human resources, medical supplies, and equipment. There are unique scientific, technical, and logistic challenges which we face in covid-19, we need to take definite steps for fights against this pandemic.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".