Assessment and Information System Establishment of the COVID-19 Impacts and countermeasures: Gray Prediction Model Applied in Analysis and Prediction
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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