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
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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