Bibliographic record
Abstract
The recent analysis by Mora and colleagues revealed that over 277 diseases can worsen due to climatic hazards resulting from greenhouse gas emissions. Specifically, more than 58% of known human diseases can be aggravated by climate change. Furthermore, there are over 1,000 pathways through which various climatic hazards have contributed to disease outbreaks, primarily due to the diversity of pathogens. This analysis also urges immediate action to address the root of the problem-reducing greenhouse gas (GHG) emissions. Numerous climatic hazards affect the incidence of human pathogenic diseases. Unfortunately, due to the complexity and multifaceted nature of the problem, there cannot be a single comprehensive solution to minimize climate-driven outbreaks. This study seeks to identify outbreaks of specific diseases categorized as epidemics, whose incidence is strongly correlated with global warming. The focus of this analysis is on (1) organizations responding to climate-related diseases to decelerate the incidence rates; (2) to call for a new disciplines in epidemiology that focuses exclusively on climate change-related prediction for future pandemics; (3) looking at the problem from the patient's point of view-how do non-medical/health professionals contribute to minimizing the spread of climate-related diseases?; (4) to analyze outbreaks vs. urbanization/pollution/increase in population density and public health policies; also (5) to verify the vaccination coverage vs. case reduction rate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".