Impact of the Implementation of Sustainable Development Goals on Neglected Tropical Diseases
Bibliographic record
Abstract
Vector-borne diseases and infectious diseases in, general, affect the health of human and animal populations. By implementing the sustainable development goals (SDGs) in neglected populations, the level of living conditions is improved thus providing better housing, improving environments in cities and other populated sites, and developing better sanitary infrastructures altogether leading to higher-quality health conditions for the said populations. Transdisciplinary approaches can make successful implementation of the SDGs to tackle simultaneously the preservation and improvement of the environment, monitoring the health of human and animal populations, and studying in-depth those interactions in nature; those components are included in the One Health approach. Non-medicalized approaches can impact the roots of health problems, reducing the vulnerability of populations to disease, poverty, and malnutrition. Different initiatives of the SDGs can be effective in educating neglected populations to prevent exposure to vector arthropods, infectious agents, and undesired encounters with snakes and their disabling and life-threatening toxins. It will be important to actualize the implementation of the SDGs as a global strategy while also prioritizing each component.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 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".