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
Contagions - either pathogens spread through contact networks or societal memes spread through social networks - impact the occurrence and character of both epidemic and endemic diseases. While computational models explore disease parameters in the context of a given contact network, these models are always subject to the caveat that reality may not be consistent with the simplified assumptions regarding contact, contagion or network structure. More - and more accurate - data on the contact dynamics between people and places could alleviate some uncertainties, and make models more robust tools for policy-makers and researchers. Properly applied, consumer electronics can serve as a valuable source of this data. Using smartphones as sensor platforms rather than personal communications devices, it is possible to record high fidelity information on a participant's location, activity level, and contacts between both people and places. This paper describes the design, architecture and a preliminary deployment of a general smartphone-based epidemiological data collection system. The dataset, gathered over one month, contains over 45 million records related to the behavioral patterns of 39 participants. We provide an initial analysis of aggregate level statistics to demonstrate the power and scope of the technique for capturing relevant data. Demonstrating the potential for such data to inform decision-making, we further perform an agent-based simulation of a flu-like illness that uses the dataset to capture aspects of both person-person and environmental (place-person) transmission. We demonstrate that the data collection is possible, valuable, and scalable and that the data can be leveraged to inform detailed models capturing more complex physical interactions than were previously feasible.
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 | 0.003 | 0.000 |
| 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) | 0.001 | 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