Perspective: Understanding the Intersection of Climate/Environmental Change, Health, Agriculture, and Improved Nutrition – A Case Study: Type 2 Diabetes
Bibliographic record
Abstract
Efforts to promote health through improved diet and nutrition demand an appreciation of the nutritional ecology that accounts for the intersection of agriculture, food systems, health, disease and a changing environment. The complexity and implications of this ecology is exemplified by current trends and efforts to address nutrition-related non-communicable diseases (NCDs), most prominently type 2 diabetes. The global prevalence of type 2 diabetes continues to rise unabated. Of particular concern is how to address the unhealthy dietary patterns that are contributing to this pandemic in a changing environment. A multi- disciplinary approach is required that will engage those communities that comprise the continuum of effort from research to translation and implementation of evidence-informed interventions, programs and policies. Using the prevention of type 2 diabetes by increasing fruit and vegetable consumption as an exemplar, we argue that the ability to effect positive change in this and other persistent nutrition-related problems can be achieved by moving away from siloed approaches that limit the integration of key components of the diet-health continuum. Ultimately the impact of preventing type 2 diabetes via increased fruit and vegetable consumption will depend on how the entire diet changes, not just fruits and vegetables. In addition, the rapidly changing physical environment that will confront our food production system going forward will also shape the interventions that are possible. Nonetheless, the proposed "team science" approach that accounts for all the elements of the nutrition ecology will better position us to achieve public health goals through safe and sustainable food systems.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".