Climate change-related foodborne zoonotic diseases and pathogens modeling
Bibliographic record
Abstract
Foodborne zoonotic diseases and pathogens related to climate change are of considerable concern for public health because they have impacts on food systems at the production, transportation, processing, storage, preparation and consumption levels. These impacts all can stand in the way of sustainable socioeconomic development and progress. Various multidimensional variables associated with the diseases and pathogens can be categorized in six subsystems: (i) ecological degradation, (ii) extreme weather events, (iii) supply chain management, (iv) food safety, (v) disaster management and (vi) public health policy. The variables related to these categories interact in a nonlinear way in complex adaptive systems. Various multidimensional variables, data management systems and advanced methods are required to model this complex issue. Hence, a model based on complex adaptive systems and blockchain technology-enabled agent-based modeling is proposed in this paper in order to assess the public health impact of foodborne zoonotic diseases and pathogens related to climate change. This model can be useful for identifying the risks and vulnerabilities related to the diseases and pathogens present in food systems.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".