ZoonosisMAGS Project (Part 2): Complementarity of a Rapid‐Prototyping Tool and of a Full‐Scale Geosimulator for Population‐Based Geosimulation of Zoonoses
Bibliographic record
Abstract
In the ZoonosisMAGS Project, we develop a generic geosimulation platform that fully takes advantage of the models presented in the previous chapter to simulate the spread of vector-borne diseases, the evolution, interactions, and mobility of the involved species’ populations immersed in a virtual landscape. This virtual landscape is specified and implemented as a virtual geographic environment (IVGE). In this chapter, we present the ZoonosisMAGS software suite, which is composed of a tool to create the IVGE from georeferenced data and a variety of data sources; a rapid prototyping MatLab geosimulator for model development, assessment, and calibration; and a C++ Full-Scale Geosimulator to simulate the zoonosis spread on large geographic areas. The MatLab tool offers a user-friendly interface that allows a user to specify the parameters of the compartment models, to select climatic scenarios, to create scenarios in relation to insect and animal behavior (i.e., import of ticks by migrating birds), and human intervention. Hence, this MatLab tool allows for the assessment, calibration, and comparison of compartment models for zoonoses. It is complementary to the C++ Full-Scale Geosimulator, which provides an efficient software for simulations carried out on large geographic areas. The complementarity of these two simulation tools is discussed.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".