Intelligent COVID Risk Aversion System
Bibliographic record
Abstract
The lack of an in-depth system for determining exposure to COVID-19 has left people with a need for an autonomous method of tracking/monitoring user habits and active COVID-19 cases. The COVID Risk Aversion System (CRS) was created to track users and how often they encounter these risks around them. This project currently uses Ontario as a testbed. The CRS system consists of two main components: an in-house server and user application. Using internal and external technologies, CRS logs how often users interact with other users who have the application and the locations they visit. A server was developed to store every location that each user encounters and then categorizes a quantified risk to that specific location based on multiple factors. Risk is determined by COVID-19 cases in the area, risk values of people at given locations, and regional per capita cases of COVID-19. The server alters area risk based on decreasing or increasing cases within a specific region. Every hour, the server checks Ontario’s COVID-19 statistics and updates the database’s values, and then recalculates the dynamic values for all locations stored in the system. The client-side application reports the user’s location every 5 minutes and requests information on all users geographically close to that person using Vincenty’s formula. Twice a day, the application updates the user’s risk based on the interactions the user has had throughout the day. Users can also view a map of Ontario that displays regional risk and can check the risk of specific locations. CRS aims to be an effective method at reducing the user’s exposure to COVID-19.
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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.013 |
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".