How can we reduce the climate costs of OHBM? A vision for a more sustainable meeting
Bibliographic record
Abstract
Climate change threatens the future of humanity. It will also significantly impede our ability toconduct science, by destabilising societies globally. Aviation, including travel to scientificconferences, generates a huge carbon footprint. This must be addressed if we are to limit globalwarming to the 1.5C mandated by the UN Intergovernmental Panel on Climate Change (IPCC),and time is running very short: we are already at 1.2C of warming. This means we must urgentlytransform the way we attend conferences.In this report, authored by the Sustainability and Environment Action Special Interest Group (SEASIG),we analysed the carbon footprint of previous Organization for Human Brain Mapping(OHBM) meetings, and found that on average, attendees travelling to an in-person meetinggenerates over 10,000 tonnes of carbon. Virtually all these emissions are eliminated when wemeet online instead. The location of in-person meetings also matters: setting the meeting in aplace that requires more colleagues to take long-distance flights very significantly increases itsclimate costs, sometimes by up to three times as much as the lowest-carbon locations.We can do things differently, however. Hybrid meetings - accessible both in-person and online -are set to become the norm for academic societies around the world. Although driven by Covid,hybrid is here to stay, because of the many other benefits it brings to both accessibility andsustainability. There are also several other alternative meeting formats being explored byacademic societies, such as a biennial meeting (every other year), and multiple regional hubs, inwhich attendees travel to their nearest geographical meeting location.Using aviation carbon footprint modelling, we calculated the carbon savings that OHBM wouldmake under these future meeting formats. We also determined the most climate-friendly locationsfor in-person aspects of future meetings, and the least climate-friendly places to avoid. As a result,we recommend that all future OHBM meetings are fully hybrid. We furthermore recommend thatOHBM transitions to a multiple regional hub model (with hybrid attendance also supported), inlocations specifically chosen to minimise long-distance aviation. We do not advocate carbonoffsetting as a suitable alternative to tackling real-time reductions in aviation emissions.We conclude that updating the way OHBM meetings are run for a post-Covid, climate-crisis-erawill save thousands of tonnes of carbon at a time of climate emergency. Furthermore, setting themeeting in locations that minimise the need for long-distance flying is critical. Finally, supportingcolleagues to attend online and more locally will enhance accessibility, furthering the society’smission to provide educational forums for the exchange of ground-breaking neuroimagingresearch.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".