The impact of COVID-19 on farmers' economic income in Hubei Province of China
Bibliographic record
Abstract
This paper discusses the statistical measurement of the impact of COVID-19 majoremergencies on farmers' economic income in Hubei Province. Hubei Province wasselected as the object of analysis, and five data of total output value of agriculture,forestry, animal husbandry, fishery and per capita disposable income of farmers inHubei Province from the first quarter of 2013 to the second quarter of 2020 werecollected by using the Internet. Since all the collected data were macroeconomic data,these data were taken the logarithm to meet the economic significance.The per capita disposable income of farmers was taken as the response variable, andthe main factors affecting farmers' income were obtained by factor analysis.Livestock husbandry and fishery industries were the main industries in HubeiProvince. Then the score of factor analysis were taken as explained variable toestablish regression model composed of influencing factors. This paper use themultiple linear regression, support vector regression to fitting and forecasting data,ARIMA model of time series analysis, introduced at the same time, through the AICmodel choice, with the first quarter of 2013 to 2019 in the second quarter fittingtraining, backward prediction two quarters, and three or four quarter of 2019compared with the real data, through to the predicted results of the sequence diagramand evaluation index model to compare the mean square error (RMSE).Three models predict per capita disposable income of farmers in the first and secondquarter of 2020. It has been found that performance better ARIMA model in themodel compare is worse than before, and three kinds of predicted values are higherthan the real value of the model, showed the outbreak to the influence of theagricultural economy in hubei province is serious.On this basis, taking into accountthe characteristics of geomorphic climate in Hubei province, the constructivesuggestions are put forward.
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.
How this classification was reachedexpand
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".