The Carbon Cost of Travel to a Medical Conference: Modelling the Annual Meeting of the Canadian Association of Gastroenterology
Bibliographic record
Abstract
Abstract Objectives We estimated and compared the travel related carbon emissions of the annual meeting of the Canadian Association of Gastroenterology between the two most common geographical locations of the meeting. Methods We modelled the car, train and flight travel journey of each registrant to two annual meetings. One was held in Toronto, close to where the majority of Gastroenterogists live, the other in Banff in the west of the country. We used validated carbon emission outputs per kilometer of travel. Results The average per capita distance travelled to the Toronto meeting was 2845 km, resulting in 0.540 tonnes (t) of CO2equivalent (CO2e) emitted per person. When the meeting was held in Banff emissions were 41% higher than those in Toronto with an average distance travelled of 3949 km and 0.760t of CO2e emitted per person. Almost all of the travel related carbon emissions for both meetings were generated by flying. Conclusions Even when held close to the largest population centre, there is a large environmental impact from travel to annual meetings. Importantly, choice of meeting location has a very big impact on difference in carbon emissions. Societies need to consider the site of meetings and reduce the number of in-person attendees if they wish to reduce their carbon footprint. Hybrid models participants should be considered. Our analysis also suggests, other medical societies who wish to model their annual meetings can use a simplified model, using flying distance only, to estimate travel-related emissions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".