Climate change and vector-borne diseases of public health significance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There has been much debate as to whether or not climate change will have, or has had, any significant effect on risk from vector-borne diseases. The debate on the former has focused on the degree to which occurrence and levels of risk of vector-borne diseases are determined by climate-dependent or independent factors, while the debate on the latter has focused on whether changes in disease incidence are due to climate at all, and/or are attributable to recent climate change. Here I review possible effects of climate change on vector-borne diseases, methods used to predict these effects and the evidence to date of changes in vector-borne disease risks that can be attributed to recent climate change. Predictions have both over- and underestimated the effects of climate change. Mostly under-estimations of effects are due to a focus only on direct effects of climate on disease ecology while more distal effects on society's capacity to control and prevent vector-borne disease are ignored. There is increasing evidence for possible impacts of recent climate change on some vector-borne diseases but for the most part, observed data series are too short (or non-existent), and impacts of climate-independent factors too great, to confidently attribute changing risk to climate change.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it