Bibliographic record
Abstract
Public health prioritizes community medical conditions and population health factors. Promoting \npopulation health and preventing disease outbreaks and epidemics are the main goals. Targeting \npopulations based on territorial factors, socio-economic and environmental determinants, and phenotypic \nprofiles is essential for developing precise preventive or health promotion measures. Digital Twins (DTs) \ntechnology enables data acquisition, hypothesis generation, and in-silico experiments and comparisons. \nThanks to Internet of Things and Artificial Intelligence, digital twins can collect a wider range of real-time \ndata from various sources in addition to traditional data sources like Electronic Health Records. Thus, \ncomprehensive simulations of physical entities, their functionality, and their evolution can be created and \nmaintained. This position paper proposes using DT technology, Public Health instruments, knowledge \ngraphs, and AI to enable Precision and Predictive Public Health for population health. In particular, it \nintroduces Neuro-symbolic DTs, which combine semantic reasoning supported by a knowledge graph, \ndeep-learning’s predictive power, and a DT’s agility to simulate public health interventions in a virtual \nenvironment.
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 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".