SynthEco: An open-source Python application for the generation of a multi-scale digital ecosystem anchored in synthetic populations
Bibliographic record
Abstract
Synthetic populations (SPs) are a special kind of synthetic datasets that are statistical representations of a population at a given geo-spatial granularity, created using individual/household and aggregated census data. The SynthEco project aims to provide a platform for researchers to create a basic SP using an open-source Python package. The basic SP is then enriched by combination with (1) other datasets that capture the many dimensions of individual/household characteristics and real-world behaviors (creating what we call mosaic agents) and (2) with real-world behavioral data for spatially and/or temporally explicit characterization of the environment in which agents evolve for analytics and/or simulations combining individual and environmental characteristics. This package allows for SP creation from any census data and on different geographic granularities and jurisdictions through a simple plug-in system enabling enrichment into multiscale digital ecosystems. The use of SynthEco is illustrated in two use cases.
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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 | 0.002 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".