Impact of a poignant pandemic COVID-19 on Indian Economy
Bibliographic record
Abstract
Coronavirus disease (COVID-19) is a contagious disease caused due to a “Severe Acute Respiratory Syndrome Coronavirus -2 virus” (SARS-COV-2). People who fall ill will experience mild to moderate fever and will retrieve without any special treatment. This pandemic was first seen at Wuhan, China in December 2019. After seen it’s dreadfulness it was declared as a “public health emergency of international concern” (World Health Organization, WHO). As on 1 May 2020 more than 35000 cases have been reported in India resulting in more than 1147 deaths in India till date. It has also led severe socio-economic global disruption. Presently significant slowdown is experienced by Indian economy over the past few quarters.To rectify sluggish consumption demand and investment a numeral of incentive measures has been taken to retrieve the economy towards prosperity. The last quarter of the current fiscal exhibits robust prospect of improvement. However, the new COVID-19 epidemic has contrived the recovery exceptionally arduous in the near to middle terms. The pandemic has thrown new threats for the Indian economy from demand as well as from the supply side. This study is descriptive. The objective of the current study is to find out the impacts of the outbreak of COVID-19 on different sectors of our country. In conclusion, this study suggests policy measures to safeguard the Indian economy from the outbreak of it and bring it back on the growth path.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".