Webinars reduce the environmental footprint of pediatric cardiology conferences
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Webinars have recently replaced in-person medical conferences, including paediatric cardiology conferences, given the COVID-19 pandemic. METHODS: With increasing environmental concerns, we analysed the differences between the environmental footprint of a paediatric cardiology webinar with a hypothetical conference. Travel data was collected, with assumptions made on the amount of computer use, internet use and accordingly the overall use of electricity for both forms of conference. Life Cycle Assessment methodology was used (OpenLCA and Ecovinvent v 3.7). RESULTS: We showed that the theoretical environmental impact of a virtual conference is significantly less (4 tons CO2 equivalent) than the traditional international face-to-face conference (192 tons CO2 equivalent). The life cycle assessment methodology showed that resource use for a face-to-face conference lasting 2.5 days for 1374 attendees is equivalent to 400 times what an average person would use in one year, the climate change and photochemical ozone formation approximately 250 times and the eutrophication terrestrial equivalent to 225 times. However, using carbon equivalent emissions to measure environmental harm from flying is an under estimate of the potential damage, when one considers the additional production of airplane contrails. Notwithstanding this, there is a 98% reduction in climate change impact when meetings are held virtually. CONCLUSIONS: While the virtual conference may never completely replace the traditional in-person paediatric cardiology conference, due to networking benefits, the significant theoretical benefits to the environment highlighted in this study, warrants consideration for the virtual conference taking a more common place in sustainable academia.
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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 | 0.001 | 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 | 0.001 |
| 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 it