The Impact of Climate Change, Pollution, and Biodiversity Loss on Digestive Health and Disease
Bibliographic record
Abstract
The environment is changing rapidly under pressure from 3 related drivers: climate change, pollution, and biodiversity loss. These environmental changes are affecting digestive health and disease in multiple ways. Heat extremes can cause intestinal and hepatic dysfunction. Access to adequate amounts of food of high nutritional content and to clean water is under threat. Extreme weather is associated with flooding and enteric infections and affects the delivery of care through infrastructure loss. Air, water, and soil pollution from chemicals and plastics are emerging as risk factors for a variety of intestinal diseases including eosinophilic esophagitis, metabolic dysfunction associated fatty liver disease, digestive tract cancers, inflammatory bowel disease, and functional bowel disease. Migration of populations to cities and between countries poses a special challenge to the delivery of digestive care. The response to the threat of environmental change is well underway in the global digestive health community, especially with regard to understanding and reducing the environmental impact of endoscopy. Individuals, and peer societies, are becoming more engaged, and have an important role to play in meeting the challenge.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.005 | 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".