Bibliographic record
Abstract
BACKGROUND It can be seen that the occurrence of disease in the destination is closely related to the health of travelers. The public urgently needs a localized disease status retrieval tool. Mini App is an application that can be used without downloading and installing. Users can scan or search from WeChat to open the application. It’s the most popular way for Chinese people to use it. OBJECTIVE This study aims to explore the feasibility of establishing disease risk communication platform system in the era of artificial intelligence and big data, to provide suggestions for disease control workers and the public to establish an information-based data platform for efficient communication, and to provide practical and scientific basis for the implementation of accurate communication strategy of risk communication. METHODS The data of authoritative portal websites and authoritative websites in related fields were collected and integrated into a practical destination disease information platform suitable for the public. The data source includes World Health Organization, National Health Commission of the people's Republic of China, China Center for Disease Control and prevention, CDC of the United States, ECDC of Europe, portal of Hong Kong Health Protection Center, etc. RESULTS A total of 946 pieces of information were collected from the National Health Commission of the people's Republic of China (300), CDC (370), World Health Organization (170), Covid-19 guidelines (79) and Canada tourism network-- travel.gc.ca （27）. The number of searches was 1134. CONCLUSIONS This small program conforms to the current new media data age, people's habits, directly facing the public, let the public understand the disease situation of the tourism destination, and obtain authoritative prevention and control guidelines; it provides information and convenience for the public, and has a good practical application prospect.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Software About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.167 | 0.088 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".