Climate Change and Type 2 Diabetes
Bibliographic record
Abstract
Diabetes is a global epidemic impacting the lives of many people on a daily basis. At present, it is estimated that 366 million people are living with diabetes globally and this number is expected to increase by 50.8 percent to 552 million by the year 2030. Paralleling the epidemic of type 2 diabetes is the phenomena of climate change, which has long been overlooked. However, these environmental changes are no longer scenarios of the future and the effects of climate change are observable today through variable weather patterns and rising sea levels, to name a few. Together, these global issues are impacting the health and well-being of the world’s most vulnerable populations, especially the health of women, children, the elderly, the poor and those in low socio-economic statuses (low SES), and those with underlying health conditions. By observing the global impact of climate change on T2D and the future changes in this metabolic disorder’s prevalence and incidence that may ensue, researchers may be able to curtail the detrimental effects of the associated comorbid conditions associated with diabetes (such as hypertension, cardiovascular disease and the Metabolic Syndrome) amongst the world’s most susceptible individuals.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".