Assessment and Information System Establishment of the COVID-19 Impacts and countermeasures: Gray Prediction Model Applied in Analysis and Prediction
Bibliographic record
Abstract
Abstract The outbreak of COVID-19 has had a huge impact on China’s economic and social development, among which the tertiary industry has been severely impacted. As the epidemic prevention and control in China has achieved initial success and entered the normal prevention and control stage, it is very necessary to analyze the damage situation of industries directly affected by the epidemic. According to the historical data of various industries in China in the past five years, a grey prediction model was established to predict the normal development law of some economic indicators without an epidemic situation. Compared with the actual values in the first two quarters of 2020, we can estimate the economic and social losses caused by the COVID-19 epidemic. The epidemic has had the most serious impact on the tertiary industry, with retail, tourism, and catering sectors were hit hard. With the effective control of the epidemic, China’s overall economic performance in the second quarter rose steadily. Many enterprises in the comprehensive service sector have been upgraded and transformed during the epidemic. From the current perspective, the epidemic will not have a serious impact on economic development throughout the year.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".