Epidemiology-based Task Assignment Algorithm for Distributed Systems
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
OBJECTIVE: Design task assignment algorithms based on the patterns of disease spread among the population. SCOPE: Epidemiology studies spatiotemporal patterns of illness in populations and the factors affecting it. An epidemic emerges out of the population activities and environment. Task assignment is a common activity in many realms where sub-tasks are created, delegated, and collectively carried out to achieve the original task. Due to its complexity and context, task assignment can be a challenging activity that can result in limited outcomes. This research studies task assignment as an epidemic assigned to a distributed system. We have developed computational models to understand the outbreak of aerosol-borne diseases by using the agent-based modelling approach. Experiments are carried out to observe the patterns of emergence during the spread of disease among the individuals and get insights of their mechanisms. These mechanisms are used to design algorithms for task assignment on distributed systems. RESULTS: Understanding the emergent behaviour of diseases can provide the platform for the development of distributed algorithms that can be helpful in overcoming some of the challenges of task assignment in a distributed system.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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 it