Assessment of the benefits of the Chinese Public Weather Service
Bibliographic record
Abstract
ABSTRACT The present study aims to understand the public opinion on the Chinese Public Weather Service and evaluate its benefits. Statistics based on a nationwide survey conducted in China in 2006 show that the receipt and perception of weather information vary across different ages and groups. People obtain weather information from various sources (television ranks first); younger generations favour new media. There is a high demand for information services related to severe weather and short‐range weather, which have the largest impact on daily lives and professional needs. The respondents' satisfaction with the current weather service and forecast accuracy are very high, although the accuracy of weather forecasts requires improvements. The benefits of the Chinese Public Weather Service and cost–benefit ratios are evaluated via direct, indirect and reverse willingness‐to‐pay evaluation models. For the entire country, the benefit of the Chinese Public Weather Service is estimated to be at least 46.482 billion Chinese Yuan (CNY), which accounted for 0.22% of the Chinese gross domestic product (GDP) in 2006; the national cost–benefit ratio is 1:26. The regional cost–benefit ratios of the Chinese Public Weather Service exhibit a wide range from 1:2 to 1:81 over different provincial‐level regions. The cost–benefit ratios are much higher in economically developed areas (Central and East China) than in economically underdeveloped areas (Northwest China). The social development level, especially certain aspects of primary industry and transportation, is closely related to the cost–benefit ratio of the Chinese Public Weather Service. These findings can assist the China Meteorological Administration and its supervised meteorological bureaus in providing a weather service that meets the public needs effectively.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".