IFEM executive summary white paper of climate and ecological crisis
Bibliographic record
Abstract
Healthcare plays a crucial role in society, but can also have unintended consequences on the environment, which can then negatively affect health.This is referred to as the environment-healthcare cycle.Healthcare has an enormous carbon footprint, contributing approximately 5% of global emissions, and is thus a major contributor to the climate and ecological crisis that our planet is now facing [1].Climate change and its related disasters cause people to seek emergency medical care.Staff working in emergency departments must, therefore, be able to recognize and treat patients with climate-related conditions.This white paper will review the effects of climate change on human health, the inequitable distribution of these negative effects, delineate how healthcare is harming the environment, and provide steps that can be taken to minimize healthcare's carbon footprint.The full, unabridged white paper can be found on the International Federation for Emergency Medicine website:
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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.015 | 0.013 |
| Insufficient payload (model declined to judge) | 0.053 | 0.036 |
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".