Canada’s changing climate: what does it mean for digestive health?
Bibliographic record
Abstract
Canadians live in one of the areas with the most rapid pace of climate change. Canada is warming at twice the global average.1 While this has some advantages, such as a longer growing season, this change represents a major public health threat.2 Global warming, driven primarily by the burning of fossil fuels, is strongly correlated with pollution of water, air, and soil. Warming and pollution can adversely affect digestive health in several ways (Figure 1). Increasing temperatures make weather events such as storms, rainfall, and dry periods more extreme, and increase the likelihood of forest fires.3 The relationship between climate change and digestive health. These changes are already threatening water quality, food production, medical infrastructure, and supply chains. There is growing evidence for a connection between pollution and a variety of digestive illnesses including esophageal, colon, and liver cancers, eosinophilic esophagitis, appendicitis, and inflammatory bowel disease.3 Current digestive health delivery contributes to the problem because, as a high-volume diagnostic and therapeutic specialty, we generate a considerable amount of waste, pollution, and greenhouse gases.4,5 Endoscopy is the third largest generator of waste in hospital departments.4 Much of the waste is incinerated, adding to the pollution of the atmosphere.6 The Canadian Association of Gastroenterology (CAG) recognizes the need to minimize the environmental harm of practice and is releasing both its sustainability plan, and the methodology used to develop it.7 The plan will form the basis of CAG’s response to climate change and will help ensure that we deliver high-quality care while minimizing environmental harm. None declared. Conflict of interest disclosure forms (ICMJE) have been collected for all co-authors and can be accessed as supplementary material here. No new data were generated or analysed in support of this manuscript.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".