SEEDNet: Covariate-free multi-country settlement-level epidemiological estimates datasets for network analysis
Bibliographic record
Abstract
The study of population health through network science is promising but suitable population health datasets covering low- and middle-income countries (LMICs) are not available. Covariate-based methods used to produce small-area estimates (SAEs) combine national health surveys with covariates from varied sources through various methods limiting their use for producing network representations of populations by injecting unquantifiable uncertainty into estimates of node attributes, affecting the comparability of representations across countries and time. Here, we present SEEDNet (Settlement-level Epidemiological Estimates Datasets for Network Analysis), a multi-country data library of population health indicators across human settlements. Our datasets are produced through a covariate-free method that uses georeferenced national surveys to produce SAEs of health indicators and include complete mapping of population settlements of all sizes. Our open-access library is intended to be used as the basis for network representations of population health in LMICs. Novel aspects include automated estimation process, harmonized data inputs, complete settlement mapping and the adoption of settlements as the functional units for network-based analysis of epidemiological data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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 source (direct Gemma or distilled Codex), 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".