The air travel carbon footprint of four recent global oral health meetings – Should we fly less?
Bibliographic record
Abstract
There is an ethical and moral obligation to balance the collegial benefits of oral health professional meetings and the environmental impacts of these meetings. The air travel carbon footprint and distance travelled by attendees of four global oral health meetings in 2023 and 2024 was estimated from publicly available data. The four meetings were the FDI congresses in Sydney in 2023 and Istanbul in 2024; and the IADR general sessions in Bogota in 2023 and New Orleans in 2024. Online calculators were used to estimate these in metric tons of carbon dioxide (Mt CO 2 e) and kilometres (km). . The total Mt CO 2 e of the four meetings was about 47,000, which is equivalent to the annual footprint of about 10,000 people. The distance travelled was about 325 million km, equating to about 425 return trips to the moon. Both Mt CO 2 e and distance travelled differed across the four meetings, with the Sydney world dental congress contributing most. The environmental impact of this limited sample of four global oral health meetings is significant. There is a need to implement strategies to reduce this impact while maintaining the importance of the knowledge exchange and scientific advancements these meetings offer. While the primary focus of the oral health profession is patient care, and the direct clinical implications of this study may not be evident, understanding and addressing the environmental impact associated with our professional activities, such as attending global oral health meetings, can lead to more sustainable practices within the profession. The oral health industry contributes to climate change through its carbon footprint from energy use, travel, and waste production. By adopting environmentally conscious practices, oral health professionals can help mitigate these impacts, thereby promoting overall planetary and human well-being and health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".